def run_univariate_function(name, symbol_fmt, func):
print('\n*************************************')
print('Testing {} function'.format(name))
# PsychSim elements
world = World()
agent = Agent('The Agent')
world.addAgent(agent)
# gets samples from real non-linear function
x_params, y_params, sample_values = \
get_bivariate_samples(func, MIN_X, MAX_X, MIN_Y, MAX_Y, NUM_SAMPLES, NUM_SAMPLES)
sample_mean = np.nanmean(sample_values)
# create two features: one holding the variable, the other the result (dependent)
var_x = world.defineState(agent.name, 'var_x', float, lo=MIN_X, hi=MAX_X)
var_y = world.defineState(agent.name, 'var_y', float, lo=MIN_Y, hi=MAX_Y)
result = world.defineState(agent.name,
'result',
float,
lo=np.min(sample_values),
hi=np.max(sample_values))
world.setFeature(result, 0)
# create action that is approximates the function, storing the result in the result feature
action = agent.addAction({'verb': 'operation', 'action': name})
tree = makeTree(
tree_from_bivariate_samples(result, var_x, var_y, x_params, y_params,
sample_values))
world.setDynamics(result, action, tree)
world.setOrder([agent.name])
np.random.seed(SEED)
values_original = []
values_approx = []
for i in range(NUM_TEST_SAMPLES):
# gets random sample parameters
x = MIN_X + np.random.rand() * (MAX_X - MIN_X)
y = MIN_Y + np.random.rand() * (MAX_Y - MIN_Y)
# sets variable and updates result
world.setFeature(var_x, x)
world.setFeature(var_y, y)
world.step()
real = func(x, y)
psych = world.getValue(result)
print('{:3}: {:30} | Expected: {:10.2f} | PsychSim: {:10.2f}'.format(
i, symbol_fmt.format(x, y), real, psych))
values_original.append(real)
values_approx.append(psych)
# gets error stats
rmse = np.sqrt(np.mean((np.array(values_approx) - values_original)**2))
print('=====================================')
print('RMSE = {:.3f}'.format(rmse))
print('\nPress \'Enter\' to continue...')
input()
False: True},
True: False},
False: False},
False: False}))
# Parameters
me.setHorizon(6)
me.setParameter('discount',0.9)
# Levels of belief
david.setRecursiveLevel(3)
stacy.setRecursiveLevel(3)
# Turn order: Uncomment the following if you want agents to act in parallel
#world.setOrder([{agts[0].name,agts[1].name}])
# Turn order: Uncomment the following if you want agents to act sequentially
world.setOrder([agts[0].name,agts[1].name])
# World state
world.defineState(None,'agreement',bool)
world.setState(None,'agreement',False)
world.defineState(None,'appleOffer',bool)
world.setState(None,'appleOffer',False)
world.defineState(None,'pearOffer',bool)
world.setState(None,'pearOffer',False)
world.defineState(None,'round',int,description='The current round of the negotiation')
world.setState(None,'round',0)
world.defineState(None,'rejectedNegotiation',bool,
description='Have one of the players walked out?')
world.setState(None, 'rejectedNegotiation', False)
class Ultimatum:
def __init__(self):
self.world = World()
stacy = Agent('Stacy')
david = Agent('David')
agts = [stacy, david]
totalAmt = 4
# Player state, actions and parameters common to both players
for i in range(2):
me = agts[i]
other = agts[1-i]
self.world.addAgent(me)
self.world.defineState(me.name,'offered',int,lo=0,hi=totalAmt)
self.world.defineState(me.name,'money',int,lo=0,hi=totalAmt)
me.setState('offered',0)
me.setState('money',0)
if (me.name == 'Stacy'):
for amt in range(totalAmt + 1):
me.addAction({'verb': 'offer','object': other.name,'amount': amt})
else:
mePass = me.addAction({'verb': 'accept','object': other.name})
mePass = me.addAction({'verb': 'reject','object': other.name})
# Parameters
me.setHorizon(2)
me.setParameter('discount',0.9)
# me.setParameter('discount',1.0)
# Levels of belief
david.setRecursiveLevel(3)
stacy.setRecursiveLevel(3)
self.world.setOrder(['Stacy','David'])
# World state
self.world.defineState(None,'gameOver',bool,description='The current round')
self.world.setState(None,'gameOver',False)
self.world.addTermination(makeTree({'if': trueRow(stateKey(None,'gameOver')),
True: True, False: False}))
# offer dynamics
atom = Action({'subject': 'Stacy','verb': 'offer', 'object': 'David'})
parties = [atom['subject'], atom['object']]
for j in range(2):
offer = stateKey(parties[j],'offered')
amount = actionKey('amount') if j == 1 else '%d-%s' % (totalAmt,actionKey('amount'))
tree = makeTree(setToConstantMatrix(offer,amount))
self.world.setDynamics(parties[j],'offered',atom,tree)
# accept dynamics
atom = Action({'subject': 'David','verb': 'accept', 'object': 'Stacy'})
parties = [atom['subject'], atom['object']]
for j in range(2):
offer = stateKey(parties[j],'offered')
money = stateKey(parties[j],'money')
tree = makeTree(setToFeatureMatrix(money,offer))
self.world.setDynamics(parties[j],'money',atom,tree)
tree=makeTree(setTrueMatrix(stateKey(None,'gameOver')))
self.world.setDynamics(None,'gameOver',atom,tree)
# reject dynamics
atom = Action({'subject': 'David','verb': 'reject', 'object': 'Stacy'})
tree=makeTree(setTrueMatrix(stateKey(None,'gameOver')))
self.world.setDynamics(None,'gameOver',atom,tree)
# really need to ask david about these levels - if adding modesl with levels, can
# the true model point to these but have a different level
for agent in self.world.agents.values():
agent.addModel('Christian',R={},level=2,rationality=25.,selection='distribution')
agent.addModel('Capitalist',R={},level=2,rationality=25.,selection='distribution')
# agent.addModel('Christian',R={},level=2,rationality=10.,selection='distribution')
# agent.addModel('Capitalist',R={},level=2,rationality=10.,selection='distribution')
# agent.addModel('Christian',R={},level=2,rationality=10.,selection='distribution')
# agent.addModel('Capitalist',R={},level=2,rationality=10.,selection='random')
def modeltest(self,trueModels,davidBeliefAboutStacy,stacyBeliefAboutDavid,strongerBelief):
agts = self.world.agents.values()
for i in range(2):
me = agts[i]
other = agts[1-i]
for model in me.models.keys():
if model is True:
name = trueModels[me.name]
else:
name = model
if name == 'Capitalist':
me.setReward(maximizeFeature(stateKey(me.name,'money')),1.0,model)
elif name == 'Christian':
me.setReward(maximizeFeature(stateKey(me.name,'money')),1.0,model)
me.setReward(maximizeFeature(stateKey(other.name,'money')),1.0,model)
weakBelief = 1.0 - strongerBelief
print weakBelief
belief = {'Christian': weakBelief,'Capitalist': weakBelief}
belief[davidBeliefAboutStacy] = strongerBelief
self.world.setMentalModel('David','Stacy',belief)
belief = {'Christian': weakBelief,'Capitalist': weakBelief}
belief[stacyBeliefAboutDavid] = strongerBelief
self.world.setMentalModel('Stacy','David',belief)
def runit(self,Msg):
print Msg
for t in range(2):
self.world.explain(self.world.step(),level=2)
# print self.world.step()
self.world.state.select()
# self.world.printState()
if self.world.terminated():
break
# this simulates over-stock cost, best is to receive max of 5, more than this has costs
ag_consumer.setReward(
makeTree({
'if':
thresholdRow(var_rcv_amnt, 5),
True:
multi_reward_matrix(ag_consumer, {
CONSTANT: 10,
var_rcv_amnt: -1
}),
False:
multi_reward_matrix(ag_consumer, {var_rcv_amnt: 1})
}), 1)
# define order (parallel execution)
world.setOrder([{ag_producer.name, ag_consumer.name}])
# sets consumer belief that producer is at half-capacity, making it believe that asking more has more advantage
# - in reality, producer is always at full capacity, so best strategy would be to always ask less
ag_consumer.setBelief(var_half_cap, True)
ag_consumer.setAttribute(
'static', True
) # set to static so that beliefs never change (to their real value)
total_rwd = 0
for i in range(NUM_STEPS):
logging.info('====================================')
logging.info('Step {}'.format(i))
step = world.step()
reward = ag_consumer.reward()
logging.info('Half capacity:\t\t{}'.format(
class _ConverterBase(object):
model: RDDL
world: World
turn_order: List[Set[str]]
def __init__(self,
normal_stds=NORMAL_STDS,
normal_bins=NORMAL_BINS,
poisson_exp_rate=POISSON_EXP_RATE):
self.features: Dict[str, str] = {}
self.constants: Dict[str, int or float or bool or str] = {}
self.actions: Dict[str, Dict[str, ActionSet]] = OrderedDict(
) # order of agents is as given in RDDL instance
self._fluent_param_types: Dict[str, List[str]] = {}
self._fluent_levels: Dict[str, int] = {}
# set distribution discretization params
self._normal_bins = np.linspace(
-normal_stds, normal_stds,
normal_bins).tolist() # gets sample value centers
self._normal_probs = stats.norm.pdf(
self._normal_bins) # gets corresponding sample probabilities
self._normal_probs = (
self._normal_probs /
self._normal_probs.sum()).tolist() # normalize to sum 1
self._poisson_exp_rate = poisson_exp_rate
def log_state(self,
features: List[str] = None,
log_actions: bool = False) -> None:
"""
Logs (INFO level) the current state of the PsychSim world.
Only prints features that were converted from RDDL.
:param List[str] features: the features whose current value are to be printed. `None` will print all
:param bool log_actions: whether to also log agents' actions
features on record.
"""
for f in self.features.values():
if features is None or f in features:
val = str(self.world.getFeature(f)).replace('\n', '\t')
logging.info(f'{f}: {val}')
for ag_name in self.actions.keys():
if log_actions:
f = actionKey(ag_name)
val = str(self.world.getFeature(f)).replace('\n', '\t')
logging.info(f'{f}: {val}')
def get_agents(self):
return list(self.world.agents.keys())
@staticmethod
def get_feature_name(f: Tuple) -> str:
"""
Gets a PsychSim feature identifier name for the given (possibly parameterized) fluent.
:param Tuple f: the (possibly parameterized) fluent, e.g., `('p', None)` or `('p', x1, y1)`.
:rtype: str
:return: the identifier string for the fluent.
"""
if isinstance(f, tuple):
f = tuple(n for n in f if n is not None)
if len(f) == 1:
f = f[0]
return re.sub(r'\'|"|@', '', str(f))
return str(f)
def _is_feature(self, name: Tuple) -> bool:
return self.get_feature_name(name) in self.features
def _get_feature(self, name: Tuple) -> str:
return self.features[self.get_feature_name(name)]
def _is_action(self, name: Tuple, agent: Agent) -> bool:
return agent.name in self.actions and self.get_feature_name(
name) in self.actions[agent.name]
def _get_action(self, name: Tuple, agent: Agent) -> ActionSet:
return self.actions[agent.name][self.get_feature_name(name)]
def _is_constant(self, name: Tuple) -> bool:
return self.get_feature_name(name) in self.constants
def _is_constant_value(self, val: str) -> bool:
return val in self.constants.values()
def _get_constant_value(self, name: Tuple) -> object:
return self.constants[self.get_feature_name(name)]
def _get_entity_name(self, name: Tuple) -> Tuple[str, Tuple]:
name = list(name)
# searches for agent name in (possibly parameterized) variable's name
for n in name:
if n in self.world.agents:
name.remove(n)
return n, tuple(name)
return WORLD, tuple(name) # defaults to world
def _is_enum(self, name: str) -> bool:
for t_name, t_vals in self.model.domain.types:
if t_name == name and isinstance(t_vals, list) and len(t_vals) > 0:
return True
return False
def _is_enum_value(self, val: str) -> bool:
for t_name, t_vals in self.model.domain.types:
if self._is_enum(t_name) and (val in t_vals
or '@' + val in t_vals):
return True
return False
def _get_enum_values(self, name: str) -> List[str] or None:
if not self._is_enum(name):
return None
for t_name, t_vals in self.model.domain.types:
if t_name == name:
return [_.replace('@', '') for _ in t_vals
] # strip "@" character from enum values
return None
def _get_domain(self, t_range: str):
# checks normal types
if t_range == 'int':
return int, 0.
if t_range == 'bool':
return bool, 0.
if t_range == 'real':
return float, 0.
# checks enumerated (domain-level constant) types
domain = self._get_enum_values(t_range)
if domain is not None:
return list, domain
# checks object (instance-level constant) types
try:
domain = self._get_param_values(t_range)
if domain is not None:
return list, domain
except ValueError:
pass
raise ValueError(f'Could not get domain for range type: {t_range}!')
def _get_param_types(self, name: str) -> List[str]:
assert name in self._fluent_param_types, \
f'Could not get param types for fluent: {name}, feature not registered!'
return self._fluent_param_types[name]
def _get_param_values(self, param_type: str) -> List[str]:
if self._is_enum(param_type): # check enum type
return self._get_enum_values(param_type)
for p_type, p_vals in self.model.non_fluents.objects: # check object instance type
if p_type == param_type:
return p_vals
raise ValueError(f'Could not get values for param type: {param_type}!')
def _get_all_param_combs(self, param_types: List[str]) -> List[Tuple]:
param_vals = [self._get_param_values(p_type) for p_type in param_types]
return list(itertools.product(*param_vals))
def _create_world_agents(self) -> None:
# create world
logging.info('__________________________________________________')
self.world = World()
# create agents from RDDL non-fluent definition, special object named "agent"
for p_type, p_vals in self.model.non_fluents.objects:
if p_type == 'agent':
for ag_name in p_vals:
self.world.addAgent(ag_name)
break
if len(self.world.agents) == 0:
self.world.addAgent(
'AGENT') # create default agent if no agents defined
# set agents' properties from instance
for agent in self.world.agents.values():
if hasattr(self.model.instance, 'horizon'):
agent.setAttribute('horizon', self.model.instance.horizon)
if hasattr(self.model.instance, 'discount'):
agent.setAttribute('discount', self.model.instance.discount)
# TODO other world and agent attributes?
agent.setAttribute('selection', 'random')
model = agent.get_true_model()
logging.info(f'Created agent "{agent.name}" with properties:')
logging.info(f'\thorizon: {agent.getAttribute("horizon", model)}')
logging.info(
f'\tdiscount: {agent.getAttribute("discount", model)}')
def _convert_constants(self):
# first try to initialize non-fluents from definition's default value
logging.info('__________________________________________________')
self.constants = {}
for nf in self.model.domain.non_fluents.values():
if nf.arity > 0:
# gets all parameter combinations
param_vals = self._get_all_param_combs(nf.param_types)
nf_combs = [(nf.name, *p_vals) for p_vals in param_vals]
else:
nf_combs = [(nf.name, None)] # not-parameterized constant
for nf_name in nf_combs:
nf_name = self.get_feature_name(nf_name)
def_val = nf.default if nf.default not in {None, 'none', 'null', 'None', 'Null'} else \
self._get_domain(nf.range)[1][0]
if isinstance(def_val, str):
def_val = def_val.replace(
'@', '') # just in case it's an enum value
self.constants[nf_name] = def_val
logging.info(
f'Initialized constant "{nf_name}" with default value "{def_val}"'
)
# then set value of non-fluents from initialization definition
if hasattr(self.model.non_fluents, 'init_non_fluent'):
for nf, def_val in self.model.non_fluents.init_non_fluent:
nf_name = nf if nf[1] is None else (nf[0], *nf[1])
nf_name = self.get_feature_name(nf_name)
if nf_name not in self.constants:
raise ValueError(
f'Trying to initialize non-existing non-fluent: {nf_name}!'
)
def_val = def_val if def_val not in {None, 'none', 'null', 'None', 'Null'} else \
self._get_domain(nf.range)[1][0]
if isinstance(def_val, str):
def_val = def_val.replace(
'@', '') # just in case it's an enum value
self.constants[nf_name] = def_val
logging.info(
f'Initialized constant "{nf_name}" in non-fluents to "{def_val}"'
)
logging.info(f'Total {len(self.constants)} constants initialized')
def _convert_variables(self):
# create features from state fluents
logging.info('__________________________________________________')
self.features = {}
for sf in self.model.domain.state_fluents.values():
self._create_features(
sf, '' if len(self.model.domain.observ_fluents) == 0 else '__')
# create features from intermediate fluents
for sf in self.model.domain.intermediate_fluents.values():
self._create_features(sf, '_')
# create features from observable fluents
for sf in self.model.domain.observ_fluents.values():
self._create_features(sf)
logging.info(f'Total {len(self.features)} features created')
def _create_features(self,
fluent: PVariable,
prefix: str = '') -> List[str]:
# registers types of parameters for this type of feature
self._fluent_param_types[fluent.name] = fluent.param_types
self._fluent_levels[fluent.name] = fluent.level if fluent.fluent_type == 'interm-fluent' else \
-1 if fluent.fluent_type == 'state-fluent' else MAX_LEVEL
# to whom should this feature be associated, agent or world?
if fluent.arity > 0:
# gets all parameter combinations
param_vals = self._get_all_param_combs(fluent.param_types)
f_combs = [(fluent.name, *p_vals) for p_vals in param_vals]
else:
f_combs = [(fluent.name, None)] # not-parameterized feature
# create and register features
feats = []
domain = self._get_domain(fluent.range)
for f_name in f_combs:
entity, feat_name = self._get_entity_name(
f_name) # tries to identify agent from fluent param comb name
f = self.world.defineState(
entity, prefix + self.get_feature_name(feat_name), *domain)
f_name = self.get_feature_name(
f_name
) # keep feature's original name for transparent later referencing
self.features[f_name] = f
# set to default value (if list assume first of list)
lo = self.world.variables[f]['lo']
def_val = fluent.default if fluent.default not in {None, 'none', 'null', 'None', 'Null'} else \
lo if lo is not None else self.world.variables[f]['elements'][0]
if isinstance(def_val, str):
def_val = def_val.replace(
'@', '') # just in case it's an enum value
self.world.setFeature(f, def_val)
logging.info(
f'Created feature "{f}" from {fluent.fluent_type} "{fluent.name}" of type "{fluent.range}"'
)
feats.append(f)
return feats
def _convert_actions(self):
# create actions for agents (assume homogeneous agents) TODO maybe put constraints in RDDL for diff agents?
logging.info('__________________________________________________')
self.actions = {agent.name: {} for agent in self.world.agents.values()}
for act_fluent in self.model.domain.action_fluents.values():
self._create_actions(act_fluent)
logging.info(
f'Total {sum(len(actions) for actions in self.actions.values())} actions created'
)
def _create_actions(self, fluent: PVariable) -> List[ActionSet]:
self._fluent_levels[
fluent.
name] = -MAX_LEVEL # for dynamics, actions always come first
if fluent.arity > 0:
# gets all parameter combinations
param_vals = self._get_all_param_combs(fluent.param_types)
act_combs = [(fluent.name, *p_vals) for p_vals in param_vals]
else:
act_combs = [(fluent.name, None)] # not-parameterized action
# create action for each param combination
actions = []
for act_name in act_combs:
entity, action_name = self._get_entity_name(
act_name
) # tries to identify agent from fluent param comb name
if entity == WORLD:
agents = self.world.agents.values(
) # create action for all agents
else:
agents = [self.world.agents[entity]
] # create action just for this agent
for agent in agents:
# keep feature's original name for transparent later referencing
act_name = self.get_feature_name(act_name)
action = agent.addAction(
{'verb': self.get_feature_name(action_name)})
actions.append(action)
self.actions[agent.name][act_name] = action
logging.info(
f'Created action "{action}" for agent "{agent.name}" from action fluent: {fluent}'
)
return actions
def _initialize_variables(self):
# initialize variables from instance def
logging.info('__________________________________________________')
for sf, val in self.model.instance.init_state:
f_name = sf if sf[1] is None else (sf[0], *sf[1])
if any(
self._is_action(f_name, agent)
for agent in self.world.agents.values()):
continue # skip action initialization
assert self._is_feature(
f_name
), f'Could not find feature "{f_name}" corresponding to fluent "{sf}"!'
f = self._get_feature(f_name)
if isinstance(val, str):
val = val.replace('@', '') # just in case it's an enum value
self.world.setFeature(f, val)
logging.info(f'Initialized feature "{f}" to "{val}"')
def _parse_requirements(self):
logging.info('__________________________________________________')
logging.info('Parsing requirements...')
agents = self.world.agents
requirements = self.model.domain.requirements
# check concurrent multiagent actions, agent order is assumed by RDDL instance definition
if len(
agents
) > 1 and requirements is not None and 'concurrent' in requirements:
if hasattr(self.model.instance, 'max_nondef_actions'):
# creates groups of agents that act in parallel according to "max_nondef_actions" param
num_parallel = self.model.instance.max_nondef_actions
ag_list = list(self.actions.keys())
self.turn_order = []
for i in range(0, len(agents), num_parallel):
self.turn_order.append(
set(ag_list[i:min(i + num_parallel, len(agents))]))
else:
self.turn_order = [set(agents.keys())
] # assumes all agents act in parallel
else:
self.turn_order = [{ag} for ag in agents.keys()
] # assumes all agents act sequentially
self.world.setOrder(self.turn_order)
# sets omega to observe only observ-fluents (ignore interm and state-fluents)
# TODO assume homogeneous agents (partial observability affects all agents the same)
if requirements is not None and 'partially-observed' in requirements:
for agent in agents.values():
observable = [actionKey(agent.name)
] # todo what do we need to put here?
for sf in self.model.domain.observ_fluents.values():
if self._is_feature(sf.name):
observable.append(self._get_feature(sf.name))
agent.omega = observable
logging.info(
f'Setting partial observability for agent "{agent.name}", omega={agent.omega}'
)
class Centipede:
def __init__(self, turnOrder, maxRounds, payoff):
self.maxRounds = maxRounds
self.payoff = payoff
print self.payoff
self.world = World()
stacy = Agent('Stacy')
david = Agent('David')
agts = [stacy, david]
# Player state, actions and parameters common to both players
for i in range(2):
me = agts[i]
other = agts[1 - i]
self.world.addAgent(me)
# State
self.world.defineState(me.name, 'money', int)
me.setState('money', 0)
mePass = me.addAction({'verb': 'pass', 'object': other.name})
meTake = me.addAction({'verb': 'take', 'object': other.name})
# Parameters
me.setHorizon(6)
me.setParameter('discount', 1.)
# me.setParameter('discount',0.9)
# Levels of belief
david.setRecursiveLevel(3)
stacy.setRecursiveLevel(3)
self.world.setOrder(turnOrder)
# World state
self.world.defineState(None,
'round',
int,
description='The current round')
self.world.setState(None, 'round', 0)
self.world.defineState(None,
'gameOver',
bool,
description='whether game is over')
self.world.setState(None, 'gameOver', False)
self.world.addTermination(
makeTree({
'if':
thresholdRow(stateKey(None, 'round'), self.maxRounds),
True:
True,
False: {
'if': trueRow(stateKey(None, 'gameOver')),
True: True,
False: False
}
}))
# Dynamics
for action in stacy.actions | david.actions:
tree = makeTree(incrementMatrix(stateKey(None, 'round'), 1))
self.world.setDynamics(None, 'round', action, tree)
if (action['verb'] == 'take'):
tree = makeTree(setTrueMatrix(stateKey(None, 'gameOver')))
self.world.setDynamics(None, 'gameOver', action, tree)
agts = ['Stacy', 'David']
for i in range(2):
key = stateKey(agts[i], 'money')
tree = makeTree(
self.buildPayoff(0, key, self.payoff[agts[i]]))
self.world.setDynamics(agts[i], 'money', action, tree)
elif action['verb'] == 'pass':
agts = ['Stacy', 'David']
for i in range(2):
key = stateKey(agts[i], 'money')
tree = makeTree({
'if':
equalRow(stateKey(None, 'round'), self.maxRounds - 1),
True:
setToConstantMatrix(
key, self.payoff[agts[i]][self.maxRounds]),
False:
noChangeMatrix(key)
})
self.world.setDynamics(agts[i], 'money', action, tree)
# really need to ask david about these levels - if adding modesl with levels, can
# the true model point to these but have a different level
for agent in self.world.agents.values():
agent.addModel('Christian',
R={},
level=2,
rationality=10.,
selection='distribution')
agent.addModel('Capitalist',
R={},
level=2,
rationality=10.,
selection='distribution')
# agent.addModel('Christian',R={},level=2,rationality=0.01)
# agent.addModel('Capitalist',R={},level=2,rationality=0.01)
def buildPayoff(self, rnd, key, payoff):
if (rnd == self.maxRounds - 1):
return setToConstantMatrix(key, payoff[rnd])
else:
return {
'if': equalRow(stateKey(None, 'round'), rnd),
True: setToConstantMatrix(key, payoff[rnd]),
False: self.buildPayoff(rnd + 1, key, payoff)
}
def modeltest(self, trueModels, davidBeliefAboutStacy,
stacyBeliefAboutDavid, strongerBelief):
agts = self.world.agents.values()
for i in range(2):
me = agts[i]
other = agts[1 - i]
for model in me.models.keys():
if model is True:
name = trueModels[me.name]
else:
name = model
if name == 'Capitalist':
me.setReward(maximizeFeature(stateKey(me.name, 'money')),
1.0, model)
elif name == 'Christian':
me.setReward(maximizeFeature(stateKey(me.name, 'money')),
1.0, model)
me.setReward(
maximizeFeature(stateKey(other.name, 'money')), 1.0,
model)
weakBelief = 1.0 - strongerBelief
belief = {'Christian': weakBelief, 'Capitalist': weakBelief}
belief[davidBeliefAboutStacy] = strongerBelief
self.world.setMentalModel('David', 'Stacy', belief)
belief = {'Christian': weakBelief, 'Capitalist': weakBelief}
belief[stacyBeliefAboutDavid] = strongerBelief
self.world.setMentalModel('Stacy', 'David', belief)
def runit(self, Msg):
print Msg
for t in range(self.maxRounds + 1):
self.world.explain(self.world.step(), level=2)
# self.world.explain(self.world.step(),level=1)
# print self.world.step()
self.world.state.select()
# self.world.printState()
if self.world.terminated():
break
world.defineState(None, 'applesOffer', bool)
world.setState(None, 'applesOffer', False)
world.defineState(None, 'pearsOffer', bool)
world.setState(None, 'pearsOffer', False)
world.termination.append(
makeTree({
'if': trueRow(stateKey(None, 'agreement')),
True: True,
False: False
}))
# Turn order: Uncomment the following if you want agents to act in parallel
# world.setOrder([[stacy.name,david.name]])
# Turn order: Uncomment the following if you want agents to act sequentially
world.setOrder([stacy.name, david.name])
# Stacy actions
stacy.addAction({'verb': 'do nothing'})
stacy.addAction({'verb': 'offerApple', 'object': david.name, 'amount': 0})
stacy.addAction({'verb': 'offerApple', 'object': david.name, 'amount': 1})
stacy.addAction({'verb': 'offerApple', 'object': david.name, 'amount': 2})
stacy.addAction({'verb': 'offerApple', 'object': david.name, 'amount': 3})
stacy.addAction({'verb': 'offerPear', 'object': david.name, 'amount': 0})
stacy.addAction({'verb': 'offerPear', 'object': david.name, 'amount': 1})
stacy.addAction({'verb': 'offerPear', 'object': david.name, 'amount': 2})
stacyAccept = stacy.addAction({
'verb': 'accept offer',
'object': david.name
class Negotiate:
def __init__(self,turnOrder):
self.maxRounds=8
self.world = World()
totals = {'apple':1,'pear':2}
batna_prePref = totals['apple'] + totals['pear']
stacy = Agent('Stacy')
david = Agent('David')
agts = [stacy, david]
# Player state, actions and parameters common to both players
for i in range(2):
me = agts[i]
other = agts[1-i]
self.world.addAgent(me)
# State
self.world.defineState(me.name,'appleOwned',int,lo=0,hi=totals['apple'])
me.setState('appleOwned',0)
self.world.defineState(me.name,'appleOffered',int,lo=0,hi=totals['apple'])
me.setState('appleOffered',0)
self.world.defineState(me.name,'pearOwned',int,lo=0,hi=totals['pear'])
me.setState('pearOwned',0)
self.world.defineState(me.name,'pearOffered',int,lo=0,hi=totals['pear'])
me.setState('pearOffered',0)
self.world.defineState(me.name,'Batna',int,lo=0,hi=10)
me.setState('Batna', batna_prePref)
self.world.defineState(me.name,'BatnaOwned',int,lo=0,hi=10)
me.setState('BatnaOwned',0)
self.world.defineState(me.name,'agree',bool)
me.setState('agree',False)
# Actions
me.addAction({'verb': 'do nothing'})
for amt in range(totals['apple'] + 1):
tmp = me.addAction({'verb': 'offerApple','object': other.name,'amount': amt})
me.setLegal(tmp,makeTree({'if': trueRow(stateKey(None, 'agreement')),
False: {'if': trueRow(stateKey(None, 'rejectedNegotiation')),
True: False,
False: True},
True: False}))
for amt in range(totals['pear'] + 1):
tmp = me.addAction({'verb': 'offerPear','object': other.name,'amount': amt})
me.setLegal(tmp,makeTree({'if': trueRow(stateKey(None, 'agreement')),
False: {'if': trueRow(stateKey(None, 'rejectedNegotiation')),
True: False,
False: True},
True: False}))
meReject = me.addAction({'verb': 'rejectNegotiation','object': other.name})
me.setLegal(meReject,makeTree({'if': trueRow(stateKey(None, 'agreement')),
False: {'if': trueRow(stateKey(None, 'rejectedNegotiation')),
True: False,
False: True},
True: False}))
meAccept = me.addAction({'verb': 'accept offer','object': other.name})
me.setLegal(meAccept,makeTree({'if': trueRow(stateKey(None, 'appleOffer')),
True: {'if': trueRow(stateKey(None, 'pearOffer')),
True: {'if': trueRow(stateKey(None, 'agreement')),
False: {'if': trueRow(stateKey(None, 'rejectedNegotiation')),
True: False,
False: True},
True: False},
False: False},
False: False}))
# Parameters
me.setHorizon(6)
me.setParameter('discount',0.9)
# Levels of belief
david.setRecursiveLevel(3)
stacy.setRecursiveLevel(3)
# Turn order: Uncomment the following if you want agents to act in parallel
# world.setOrder([{agts[0].name,agts[1].name}])
# Turn order: Uncomment the following if you want agents to act sequentially
self.world.setOrder(turnOrder)
# World state
self.world.defineState(None,'agreement',bool)
self.world.setState(None,'agreement',False)
self.world.defineState(None,'appleOffer',bool)
self.world.setState(None,'appleOffer',False)
self.world.defineState(None,'pearOffer',bool)
self.world.setState(None,'pearOffer',False)
self.world.defineState(None,'round',int,description='The current round of the negotiation')
self.world.setState(None,'round',0)
self.world.defineState(None,'rejectedNegotiation',bool,
description='Have one of the players walked out?')
self.world.setState(None, 'rejectedNegotiation', False)
# dont terminate so agent sees benefit of early agreement
# world.addTermination(makeTree({'if': trueRow(stateKey(None,'agreement')),
# True: True,
# False: False}))
# world.addTermination(makeTree({'if': trueRow(stateKey(None,'rejectedNegotiation')),
# True: True,
# False: False}))
self.world.addTermination(makeTree({'if': thresholdRow(stateKey(None,'round'),self.maxRounds),
True: True, False: False}))
# Dynamics of offers
agents = [david.name,stacy.name]
for i in range(2):
for fruit in ['apple','pear']:
atom = Action({'subject': agents[i],'verb': 'offer%s' % (fruit.capitalize()),
'object': agents[1-i]})
parties = [atom['subject'], atom['object']]
for j in range(2):
# Set offer amount
offer = stateKey(parties[j],'%sOffered' % (fruit))
amount = actionKey('amount') if j == 1 else '%d-%s' % (totals[fruit],actionKey('amount'))
tree = makeTree(setToConstantMatrix(offer,amount))
self.world.setDynamics(parties[j],'%sOffered' % (fruit),atom,tree)
# reset agree flags whenever an offer is made
agreeFlag = stateKey(parties[j],'agree')
tree = makeTree(setFalseMatrix(agreeFlag))
self.world.setDynamics(parties[j],'agree',atom,tree)
# Offers set offer flag in world state
tree = makeTree(setTrueMatrix(stateKey(None,'%sOffer' % (fruit))))
self.world.setDynamics(None,'%sOffer' % (fruit) ,atom,tree)
# agents = [david.name,stacy.name]
# Dynamics of agreements
for i in range(2):
atom = Action({'subject': agents[i],'verb': 'accept offer', 'object': agents[1-i]})
# accept offer sets accept
tree = makeTree(setTrueMatrix(stateKey(atom['subject'],'agree')))
self.world.setDynamics(atom['subject'],'agree',atom,tree)
# accept offer sets agreement if object has accepted
tree = makeTree({'if': trueRow(stateKey(atom['object'],'agree')),
True: setTrueMatrix(stateKey(None,'agreement')),
False: noChangeMatrix(stateKey(None,'agreement'))})
self.world.setDynamics(None,'agreement',atom,tree)
# Accepting offer sets ownership
parties = [atom['subject'], atom['object']]
for fruit in ['apple','pear']:
# atom = Action({'subject': agents[i],'verb': 'accept offer', 'object': agents[1-i]})
for j in range(2):
offer = stateKey(parties[j],'%sOffered' % (fruit))
owned = stateKey(parties[j],'%sOwned' % (fruit))
tree = makeTree({'if': trueRow(stateKey(atom['object'],'agree')),
False: noChangeMatrix(owned),
True: setToFeatureMatrix(owned,offer)})
self.world.setDynamics(parties[j],'%sOwned' % (fruit),atom,tree)
# rejecting give us batna and ends negotiation
atom = Action({'subject': agents[i],'verb': 'rejectNegotiation',
'object': agents[1-i]})
tree = makeTree(setToFeatureMatrix(stateKey(atom['subject'],'BatnaOwned') ,stateKey(atom['subject'], 'Batna')))
self.world.setDynamics(atom['subject'],'BatnaOwned' ,atom,tree)
tree = makeTree(setToFeatureMatrix(stateKey(atom['object'],'BatnaOwned') ,stateKey(atom['object'], 'Batna')))
self.world.setDynamics(atom['object'],'BatnaOwned' ,atom,tree)
tree = makeTree(setTrueMatrix(stateKey(None,'rejectedNegotiation')))
self.world.setDynamics(None,'rejectedNegotiation' ,atom,tree)
for action in stacy.actions | david.actions:
tree = makeTree(incrementMatrix(stateKey(None,'round'),1))
self.world.setDynamics(None,'round',action,tree)
for agent in self.world.agents.values():
agent.addModel('pearLover',R={},level=2,rationality=0.01)
agent.addModel('appleLover',R={},level=2,rationality=0.01)
def modeltest(self,trueModels,davidBeliefAboutStacy,stacyBeliefAboutDavid,strongerBelief):
for agent in self.world.agents.values():
for model in agent.models.keys():
if model is True:
name = trueModels[agent.name]
else:
name = model
if name == 'appleLover':
agent.setReward(maximizeFeature(stateKey(agent.name,'appleOwned')),4.0,model)
agent.setReward(maximizeFeature(stateKey(agent.name,'pearOwned')),1.0,model)
agent.setReward(maximizeFeature(stateKey(agent.name,'BatnaOwned')),0.1,model)
elif name == 'pearLover':
agent.setReward(maximizeFeature(stateKey(agent.name,'appleOwned')),1.0,model)
agent.setReward(maximizeFeature(stateKey(agent.name,'pearOwned')),4.0,model)
agent.setReward(maximizeFeature(stateKey(agent.name,'BatnaOwned')),0.1,model)
weakBelief = 1.0 - strongerBelief
belief = {'pearLover': weakBelief,'appleLover': weakBelief}
belief[davidBeliefAboutStacy] = strongerBelief
self.world.setMentalModel('David','Stacy',belief)
belief = {'pearLover': weakBelief,'appleLover': weakBelief}
belief[stacyBeliefAboutDavid] = strongerBelief
self.world.setMentalModel('Stacy','David',belief)
def runit(self,Msg):
print Msg
for t in range(self.maxRounds + 1):
self.world.explain(self.world.step(),level=1)
# print self.world.step()
self.world.state.select()
# self.world.printState()
if self.world.terminated():
break
class Ultimatum:
def __init__(self):
self.world = World()
stacy = Agent('Stacy')
david = Agent('David')
agts = [stacy, david]
totalAmt = 4
# Player state, actions and parameters common to both players
for i in range(2):
me = agts[i]
other = agts[1 - i]
self.world.addAgent(me)
self.world.defineState(me.name, 'offered', int, lo=0, hi=totalAmt)
self.world.defineState(me.name, 'money', int, lo=0, hi=totalAmt)
me.setState('offered', 0)
me.setState('money', 0)
if (me.name == 'Stacy'):
for amt in range(totalAmt + 1):
me.addAction({
'verb': 'offer',
'object': other.name,
'amount': amt
})
else:
mePass = me.addAction({'verb': 'accept', 'object': other.name})
mePass = me.addAction({'verb': 'reject', 'object': other.name})
# Parameters
me.setHorizon(2)
me.setParameter('discount', 0.9)
# me.setParameter('discount',1.0)
# Levels of belief
david.setRecursiveLevel(3)
stacy.setRecursiveLevel(3)
self.world.setOrder(['Stacy', 'David'])
# World state
self.world.defineState(None,
'gameOver',
bool,
description='The current round')
self.world.setState(None, 'gameOver', False)
self.world.addTermination(
makeTree({
'if': trueRow(stateKey(None, 'gameOver')),
True: True,
False: False
}))
# offer dynamics
atom = Action({'subject': 'Stacy', 'verb': 'offer', 'object': 'David'})
parties = [atom['subject'], atom['object']]
for j in range(2):
offer = stateKey(parties[j], 'offered')
amount = actionKey('amount') if j == 1 else '%d-%s' % (
totalAmt, actionKey('amount'))
tree = makeTree(setToConstantMatrix(offer, amount))
self.world.setDynamics(parties[j], 'offered', atom, tree)
# accept dynamics
atom = Action({
'subject': 'David',
'verb': 'accept',
'object': 'Stacy'
})
parties = [atom['subject'], atom['object']]
for j in range(2):
offer = stateKey(parties[j], 'offered')
money = stateKey(parties[j], 'money')
tree = makeTree(setToFeatureMatrix(money, offer))
self.world.setDynamics(parties[j], 'money', atom, tree)
tree = makeTree(setTrueMatrix(stateKey(None, 'gameOver')))
self.world.setDynamics(None, 'gameOver', atom, tree)
# reject dynamics
atom = Action({
'subject': 'David',
'verb': 'reject',
'object': 'Stacy'
})
tree = makeTree(setTrueMatrix(stateKey(None, 'gameOver')))
self.world.setDynamics(None, 'gameOver', atom, tree)
# really need to ask david about these levels - if adding modesl with levels, can
# the true model point to these but have a different level
for agent in self.world.agents.values():
agent.addModel('Christian',
R={},
level=2,
rationality=25.,
selection='distribution')
agent.addModel('Capitalist',
R={},
level=2,
rationality=25.,
selection='distribution')
# agent.addModel('Christian',R={},level=2,rationality=10.,selection='distribution')
# agent.addModel('Capitalist',R={},level=2,rationality=10.,selection='distribution')
# agent.addModel('Christian',R={},level=2,rationality=10.,selection='distribution')
# agent.addModel('Capitalist',R={},level=2,rationality=10.,selection='random')
def modeltest(self, trueModels, davidBeliefAboutStacy,
stacyBeliefAboutDavid, strongerBelief):
agts = self.world.agents.values()
for i in range(2):
me = agts[i]
other = agts[1 - i]
for model in me.models.keys():
if model is True:
name = trueModels[me.name]
else:
name = model
if name == 'Capitalist':
me.setReward(maximizeFeature(stateKey(me.name, 'money')),
1.0, model)
elif name == 'Christian':
me.setReward(maximizeFeature(stateKey(me.name, 'money')),
1.0, model)
me.setReward(
maximizeFeature(stateKey(other.name, 'money')), 1.0,
model)
weakBelief = 1.0 - strongerBelief
print weakBelief
belief = {'Christian': weakBelief, 'Capitalist': weakBelief}
belief[davidBeliefAboutStacy] = strongerBelief
self.world.setMentalModel('David', 'Stacy', belief)
belief = {'Christian': weakBelief, 'Capitalist': weakBelief}
belief[stacyBeliefAboutDavid] = strongerBelief
self.world.setMentalModel('Stacy', 'David', belief)
def runit(self, Msg):
print Msg
for t in range(2):
self.world.explain(self.world.step(), level=2)
# print self.world.step()
self.world.state.select()
# self.world.printState()
if self.world.terminated():
break
# World state
world.defineState(None,'agreement',bool)
world.setState(None,'agreement',False)
world.defineState(None,'applesOffer',bool)
world.setState(None,'applesOffer',False)
world.defineState(None,'pearsOffer',bool)
world.setState(None,'pearsOffer',False)
world.termination.append(makeTree({'if': trueRow(stateKey(None,'agreement')),
True: True,
False: False}))
# Turn order: Uncomment the following if you want agents to act in parallel
# world.setOrder([[stacy.name,david.name]])
# Turn order: Uncomment the following if you want agents to act sequentially
world.setOrder([stacy.name,david.name])
# Stacy actions
stacy.addAction({'verb': 'do nothing'})
stacy.addAction({'verb': 'offerApple','object': david.name,'amount': 0})
stacy.addAction({'verb': 'offerApple','object': david.name,'amount': 1})
stacy.addAction({'verb': 'offerApple','object': david.name,'amount': 2})
stacy.addAction({'verb': 'offerApple','object': david.name,'amount': 3})
stacy.addAction({'verb': 'offerPear','object': david.name,'amount': 0})
stacy.addAction({'verb': 'offerPear','object': david.name,'amount': 1})
stacy.addAction({'verb': 'offerPear','object': david.name,'amount': 2})
stacyAccept = stacy.addAction({'verb': 'accept offer','object': david.name})
# David actions
# define first agent's action (counter increment)
action = agent1.addAction({'verb': '', 'action': 'increment'})
tree = makeTree(
multi_set_matrix(var_counter, {
var_counter: 1,
CONSTANT: 1
}))
world.setDynamics(var_counter, action, tree)
# define second agent's action (var is copy from counter)
action = agent2.addAction({'verb': '', 'action': 'copy'})
tree = makeTree(setToFeatureMatrix(var_copy, var_counter))
world.setDynamics(var_copy, action, tree)
world.setOrder(turn_order)
# resets vars
world.setFeature(var_copy, 0)
world.setFeature(var_counter, 0)
print('====================================')
print(label)
# steps
for i in range(4):
print('Step {}, decision by: {}'.format(
str(i), turn_order[i % len(turn_order)]))
step = world.step()
counter = next(iter(world.getFeature(var_counter).keys()))
counter_cp = next(iter(world.getFeature(var_copy).keys()))
def setup():
global args
np.random.seed(args.seed)
# create world and add agents
world = World()
world.memory = False
world.parallel = args.parallel
agents = []
agent_features = {}
for ag in range(args.agents):
agent = Agent('Agent' + str(ag))
world.addAgent(agent)
agents.append(agent)
# set agent's params
agent.setAttribute('discount', 1)
agent.setHorizon(args.horizon)
# add features, initialize at random
features = []
agent_features[agent] = features
for f in range(args.features_agent):
feat = world.defineState(agent.name, 'Feature{}'.format(f), int, lo=0, hi=1000)
world.setFeature(feat, np.random.randint(0, MAX_FEATURE_VALUE))
features.append(feat)
# set random reward function
agent.setReward(maximizeFeature(np.random.choice(features), agent.name), 1)
# add mental copy of true model and make it static (we do not have beliefs in the models)
agent.addModel(get_fake_model_name(agent), parent=get_true_model_name(agent))
agent.setAttribute('static', True, get_fake_model_name(agent))
# add actions
for ac in range(args.actions):
action = agent.addAction({'verb': '', 'action': 'Action{}'.format(ac)})
i = ac
while i + args.features_action < args.features_agent:
weights = {}
for j in range(args.features_action):
weights[features[i + j + 1]] = 1
tree = makeTree(multi_set_matrix(features[i], weights))
world.setDynamics(features[i], action, tree)
i += args.features_action
# define order
world.setOrder([set(ag.name for ag in agents)])
for agent in agents:
# test belief update:
# - set a belief in one feature to the actual initial value (should not change outcomes)
# world.setModel(agent.name, Distribution({True: 1.0}))
rand_feat = np.random.choice(agent_features[agent])
agent.setBelief(rand_feat, world.getValue(rand_feat))
print('{} will always observe {}={}'.format(agent.name, rand_feat, world.getValue(rand_feat)))
# set mental model of each agent in all other agents
for i in range(args.agents):
for j in range(i + 1, args.agents):
world.setMentalModel(agents[i].name, agents[j].name, Distribution({get_fake_model_name(agents[j]): 1}))
world.setMentalModel(agents[j].name, agents[i].name, Distribution({get_fake_model_name(agents[i]): 1}))
return world
if __name__ == '__main__':
# create world and add agent
world = World()
agent = Agent('Agent')
world.addAgent(agent)
# add variable
feat = world.defineState(agent.name, 'x', float, lo=LOW, hi=HIGH)
# add single action that discretizes the feature
action = agent.addAction({'verb': '', 'action': 'discretize'})
tree = makeTree(discretization_tree(world, feat, NUM_BINS))
world.setDynamics(feat, action, tree)
world.setOrder([{agent.name}])
print('====================================')
print('High:\t{}'.format(HIGH))
print('Low:\t{}'.format(LOW))
print('Bins:\t{}'.format(NUM_BINS))
print('\nSamples/steps:')
values_original = []
values_discrete = []
for i in range(NUM_SAMPLES):
num = np.random.uniform(LOW, HIGH)
world.setFeature(feat, num)
before = world.getValue(feat)
world.step()
class Centipede:
def __init__(self,turnOrder,maxRounds,payoff):
self.maxRounds=maxRounds
self.payoff = payoff
print self.payoff
self.world = World()
stacy = Agent('Stacy')
david = Agent('David')
agts = [stacy, david]
# Player state, actions and parameters common to both players
for i in range(2):
me = agts[i]
other = agts[1-i]
self.world.addAgent(me)
# State
self.world.defineState(me.name,'money',int)
me.setState('money',0)
mePass = me.addAction({'verb': 'pass','object': other.name})
meTake = me.addAction({'verb': 'take','object': other.name})
# Parameters
me.setHorizon(6)
me.setAttribute('discount',1.)
# me.setAttribute('discount',0.9)
# Levels of belief
david.setRecursiveLevel(3)
stacy.setRecursiveLevel(3)
self.world.setOrder(turnOrder)
# World state
self.world.defineState(None,'round',int,description='The current round')
self.world.setState(None,'round',0)
self.world.defineState(None,'gameOver',bool,description='whether game is over')
self.world.setState(None,'gameOver',False)
self.world.addTermination(makeTree({'if': thresholdRow(stateKey(None,'round'),self.maxRounds),
True: True,
False: {'if': trueRow(stateKey(None,'gameOver')),
True: True,
False: False}}))
# Dynamics
for action in stacy.actions | david.actions:
tree = makeTree(incrementMatrix(stateKey(None,'round'),1))
self.world.setDynamics(stateKey(None,'round'),action,tree)
if (action['verb'] == 'take'):
tree = makeTree(setTrueMatrix(stateKey(None,'gameOver')))
self.world.setDynamics(stateKey(None,'gameOver'),action,tree)
agts = ['Stacy','David']
for i in range(2):
key = stateKey(agts[i],'money')
tree = makeTree(self.buildPayoff(0, key, self.payoff[agts[i]]))
self.world.setDynamics(stateKey(agts[i],'money'),action,tree)
elif action['verb'] == 'pass':
agts = ['Stacy','David']
for i in range(2):
key = stateKey(agts[i],'money')
tree = makeTree({'if': equalRow(stateKey(None,'round'),self.maxRounds-1),
True: setToConstantMatrix(key,self.payoff[agts[i]][self.maxRounds]),
False: noChangeMatrix(key)})
self.world.setDynamics(stateKey(agts[i],'money'),action,tree)
# really need to ask david about these levels - if adding modesl with levels, can
# the true model point to these but have a different level
for agent in self.world.agents.values():
agent.addModel('Christian',R={},level=2,rationality=10.,selection='distribution')
agent.addModel('Capitalist',R={},level=2,rationality=10.,selection='distribution')
# agent.addModel('Christian',R={},level=2,rationality=0.01)
# agent.addModel('Capitalist',R={},level=2,rationality=0.01)
def buildPayoff(self,rnd,key,payoff):
if (rnd == self.maxRounds - 1):
return setToConstantMatrix(key,payoff[rnd])
else:
return {'if': equalRow(stateKey(None,'round'),rnd),
True: setToConstantMatrix(key,payoff[rnd]),
False: self.buildPayoff(rnd+1,key,payoff)}
def modeltest(self,trueModels,davidBeliefAboutStacy,stacyBeliefAboutDavid,strongerBelief):
agts = self.world.agents.values()
for i in range(2):
me = agts[i]
other = agts[1-i]
for model in me.models.keys():
if model is True:
name = trueModels[me.name]
else:
name = model
if name == 'Capitalist':
me.setReward(maximizeFeature(stateKey(me.name,'money')),1.0,model)
elif name == 'Christian':
me.setReward(maximizeFeature(stateKey(me.name,'money')),1.0,model)
me.setReward(maximizeFeature(stateKey(other.name,'money')),1.0,model)
weakBelief = 1.0 - strongerBelief
belief = {'Christian': weakBelief,'Capitalist': weakBelief}
belief[davidBeliefAboutStacy] = strongerBelief
self.world.setMentalModel('David','Stacy',belief)
belief = {'Christian': weakBelief,'Capitalist': weakBelief}
belief[stacyBeliefAboutDavid] = strongerBelief
self.world.setMentalModel('Stacy','David',belief)
def runit(self,Msg):
print Msg
for t in range(self.maxRounds + 1):
self.world.explain(self.world.step(),level=2)
# self.world.explain(self.world.step(),level=1)
# print self.world.step()
#self.world.state.select()
# self.world.printState()
if self.world.terminated():
break
def scenarioCreationUseCase(enemy='Sylvania',model='powell',web=False,
fCollapse=None,sCollapse=None,maxRounds=15):
"""
An example of how to create a scenario
@param enemy: the name of the agent-controlled side, i.e., Freedonia's opponent (default: Sylvania)
@type enemy: str
@param model: which model do we use (default is "powell")
@type model: powell or slantchev
@param web: if C{True}, then create the web-based experiment scenario (default: C{False})
@type web: bool
@param fCollapse: the probability that Freedonia collapses (under powell, default: 0.1) or loses battle (under slantchev, default: 0.7)
@type fCollapse: float
@param sCollapse: the probability that Sylvania collapses, under powell (default: 0.1)
@type sCollapse: float
@param maxRounds: the maximum number of game rounds (default: 15)
@type maxRounds: int
@return: the scenario created
@rtype: L{World}
"""
# Handle defaults for battle probabilities, under each model
posLo = 0
posHi = 10
if fCollapse is None:
if model == 'powell':
fCollapse = 0.1
elif model == 'slantchev':
fCollapse = 0.7
if sCollapse is None:
sCollapse = 0.1
# Create scenario
world = World()
# Agents
free = Agent('Freedonia')
world.addAgent(free)
sylv = Agent(enemy)
world.addAgent(sylv)
# User state
world.defineState(free.name,'troops',int,lo=0,hi=50000,
description='Number of troops you have left')
free.setState('troops',40000)
world.defineState(free.name,'territory',int,lo=0,hi=100,
description='Percentage of disputed territory owned by you')
free.setState('territory',15)
world.defineState(free.name,'cost',int,lo=0,hi=50000,
description='Number of troops %s loses in an attack' % (free.name))
free.setState('cost',2000)
world.defineState(free.name,'position',int,lo=posLo,hi=posHi,
description='Current status of war (%d=%s is winner, %d=you are winner)' % (posLo,sylv.name,posHi))
free.setState('position',5)
world.defineState(free.name,'offered',int,lo=0,hi=100,
description='Percentage of disputed territory that %s last offered to you' % (sylv.name))
free.setState('offered',0)
if model == 'slantchev':
# Compute new value for territory only *after* computing new value for position
world.addDependency(stateKey(free.name,'territory'),stateKey(free.name,'position'))
# Agent state
world.defineState(sylv.name,'troops',int,lo=0,hi=500000,
description='Number of troops %s has left' % (sylv.name))
sylv.setState('troops',30000)
world.defineState(sylv.name,'cost',int,lo=0,hi=50000,
description='Number of troops %s loses in an attack' % (sylv.name))
sylv.setState('cost',2000)
world.defineState(sylv.name,'offered',int,lo=0,hi=100,
description='Percentage of disputed territory that %s last offered to %s' % (free.name,sylv.name))
sylv.setState('offered',0)
# World state
world.defineState(None,'treaty',bool,
description='Have the two sides reached an agreement?')
world.setState(None,'treaty',False)
# Stage of negotiation, illustrating the use of an enumerated state feature
world.defineState(None,'phase',list,['offer','respond','rejection','end','paused','engagement'],
description='The current stage of the negotiation game')
world.setState(None,'phase','paused')
# Game model, static descriptor
world.defineState(None,'model',list,['powell','slantchev'],
description='The model underlying the negotiation game')
world.setState(None,'model',model)
# Round of negotiation
world.defineState(None,'round',int,description='The current round of the negotiation')
world.setState(None,'round',0)
if not web:
# Relationship value
key = world.defineRelation(free.name,sylv.name,'trusts')
world.setFeature(key,0.)
# Game over if there is a treaty
world.addTermination(makeTree({'if': trueRow(stateKey(None,'treaty')),
True: True, False: False}))
# Game over if Freedonia has no territory
world.addTermination(makeTree({'if': thresholdRow(stateKey(free.name,'territory'),1),
True: False, False: True}) )
# Game over if Freedonia has all the territory
world.addTermination(makeTree({'if': thresholdRow(stateKey(free.name,'territory'),99),
True: True, False: False}))
# Game over if number of rounds exceeds limit
world.addTermination(makeTree({'if': thresholdRow(stateKey(None,'round'),maxRounds),
True: True, False: False}))
# Turn order: Uncomment the following if you want agents to act in parallel
# world.setOrder([set(world.agents.keys())])
# Turn order: Uncomment the following if you want agents to act sequentially
world.setOrder([free.name,sylv.name])
# User actions
freeBattle = free.addAction({'verb': 'attack','object': sylv.name})
for amount in range(20,100,20):
free.addAction({'verb': 'offer','object': sylv.name,'amount': amount})
if model == 'powell':
# Powell has null stages
freeNOP = free.addAction({'verb': 'continue'})
elif model == 'slantchev':
# Slantchev has both sides receiving offers
free.addAction({'verb': 'accept offer','object': sylv.name})
free.addAction({'verb': 'reject offer','object': sylv.name})
# Agent actions
sylvBattle = sylv.addAction({'verb': 'attack','object': free.name})
sylvAccept = sylv.addAction({'verb': 'accept offer','object': free.name})
sylvReject = sylv.addAction({'verb': 'reject offer','object': free.name})
if model == 'powell':
# Powell has null stages
sylvNOP = sylv.addAction({'verb': 'continue'})
elif model == 'slantchev':
# Slantchev has both sides making offers
for amount in range(10,100,10):
sylv.addAction({'verb': 'offer','object': free.name,'amount': amount})
# Restrictions on when actions are legal, based on phase of game
for action in filterActions({'verb': 'offer'},free.actions | sylv.actions):
agent = world.agents[action['subject']]
agent.setLegal(action,makeTree({'if': equalRow(stateKey(None,'phase'),'offer'),
True: True, # Offers are legal in the offer phase
False: False})) # Offers are illegal in all other phases
if model == 'powell':
# Powell has a special rejection phase
for action in [freeNOP,freeBattle]:
free.setLegal(action,makeTree({'if': equalRow(stateKey(None,'phase'),'rejection'),
True: True, # Attacking and doing nothing are legal only in rejection phase
False: False})) # Attacking and doing nothing are illegal in all other phases
# Once offered, agent can respond
if model == 'powell':
# Under Powell, only Sylvania has to respond, and it can attack
responses = [sylvBattle,sylvAccept,sylvReject]
elif model == 'slantchev':
# Under Slantchev, only accept/reject
responses = filterActions({'verb': 'accept offer'},free.actions | sylv.actions)
responses += filterActions({'verb': 'reject offer'},free.actions | sylv.actions)
for action in responses:
agent = world.agents[action['subject']]
agent.setLegal(action,makeTree({'if': equalRow(stateKey(None,'phase'),'respond'),
True: True, # Offeree must act in the response phase
False: False})) # Offeree cannot act in any other phase
if model == 'powell':
# NOP is legal in exactly opposite situations to all other actions
sylv.setLegal(sylvNOP,makeTree({'if': equalRow(stateKey(None,'phase'),'end'),
True: True, # Sylvania does not do anything in the null phase after Freedonia responds to rejection
False: False})) # Sylvania must act in its other phases
if model == 'slantchev':
# Attacking legal only under engagement phase
for action in filterActions({'verb': 'attack'},free.actions | sylv.actions):
agent = world.agents[action['subject']]
agent.setLegal(action,makeTree({'if': equalRow(stateKey(None,'phase'),'engagement'),
True: True, # Attacking legal only in engagement
False: False})) # Attacking legal every other phase
# Goals for Freedonia
goalFTroops = maximizeFeature(stateKey(free.name,'troops'))
free.setReward(goalFTroops,1.)
goalFTerritory = maximizeFeature(stateKey(free.name,'territory'))
free.setReward(goalFTerritory,1.)
# Goals for Sylvania
goalSTroops = maximizeFeature(stateKey(sylv.name,'troops'))
sylv.setReward(goalSTroops,1.)
goalSTerritory = minimizeFeature(stateKey(free.name,'territory'))
sylv.setReward(goalSTerritory,1.)
# Possible goals applicable to both
goalAgreement = maximizeFeature(stateKey(None,'treaty'))
# Silly goal, provided as an example of an achievement goal
goalAchieve = achieveFeatureValue(stateKey(None,'phase'),'respond')
# Horizons
if model == 'powell':
free.setAttribute('horizon',4)
sylv.setAttribute('horizon',4)
elif model == 'slantchev':
free.setAttribute('horizon',6)
sylv.setAttribute('horizon',6)
# Discount factors
free.setAttribute('discount',-1)
sylv.setAttribute('discount',-1)
# Levels of belief
free.setRecursiveLevel(2)
sylv.setRecursiveLevel(2)
# Dynamics of battle
freeTroops = stateKey(free.name,'troops')
freeTerr = stateKey(free.name,'territory')
sylvTroops = stateKey(sylv.name,'troops')
# Effect of fighting
for action in filterActions({'verb': 'attack'},free.actions | sylv.actions):
# Effect on troops (cost of battle)
tree = makeTree(addFeatureMatrix(freeTroops,stateKey(free.name,'cost'),-1.))
world.setDynamics(freeTroops,action,tree,enforceMin=not web)
tree = makeTree(addFeatureMatrix(sylvTroops,stateKey(sylv.name,'cost'),-1.))
world.setDynamics(sylvTroops,action,tree,enforceMin=not web)
if model == 'powell':
# Effect on territory (probability of collapse)
tree = makeTree({'distribution': [
({'distribution': [(setToConstantMatrix(freeTerr,100),1.-fCollapse), # Sylvania collapses, Freedonia does not
(noChangeMatrix(freeTerr), fCollapse)]}, # Both collapse
sCollapse),
({'distribution': [(setToConstantMatrix(freeTerr,0),fCollapse), # Freedonia collapses, Sylvania does not
(noChangeMatrix(freeTerr), 1.-fCollapse)]}, # Neither collapses
1.-sCollapse)]})
world.setDynamics(freeTerr,action,tree)
elif model == 'slantchev':
# Effect on position
pos = stateKey(free.name,'position')
tree = makeTree({'distribution': [(incrementMatrix(pos,1),1.-fCollapse), # Freedonia wins battle
(incrementMatrix(pos,-1),fCollapse)]}) # Freedonia loses battle
world.setDynamics(pos,action,tree)
# Effect on territory
tree = makeTree({'if': thresholdRow(pos,posHi-.5),
True: setToConstantMatrix(freeTerr,100), # Freedonia won
False: {'if': thresholdRow(pos,posLo+.5),
True: noChangeMatrix(freeTerr),
False: setToConstantMatrix(freeTerr,0)}}) # Freedonia lost
world.setDynamics(freeTerr,action,tree)
# Dynamics of offers
for index in range(2):
atom = Action({'subject': world.agents.keys()[index],'verb': 'offer',
'object': world.agents.keys()[1-index]})
if atom['subject'] == free.name or model != 'powell':
offer = stateKey(atom['object'],'offered')
amount = actionKey('amount')
tree = makeTree({'if': trueRow(stateKey(None,'treaty')),
True: noChangeMatrix(offer),
False: setToConstantMatrix(offer,amount)})
world.setDynamics(offer,atom,tree,enforceMax=not web)
# Dynamics of treaties
for action in filterActions({'verb': 'accept offer'},free.actions | sylv.actions):
# Accepting an offer means that there is now a treaty
key = stateKey(None,'treaty')
tree = makeTree(setTrueMatrix(key))
world.setDynamics(key,action,tree)
# Accepting offer sets territory
offer = stateKey(action['subject'],'offered')
territory = stateKey(free.name,'territory')
if action['subject'] == free.name:
# Freedonia accepts sets territory to last offer
tree = makeTree(setToFeatureMatrix(territory,offer))
world.setDynamics(freeTerr,action,tree)
else:
# Sylvania accepts sets territory to 1-last offer
tree = makeTree(setToFeatureMatrix(territory,offer,pct=-1.,shift=100.))
world.setDynamics(freeTerr,action,tree)
# Dynamics of phase
phase = stateKey(None,'phase')
roundKey = stateKey(None,'round')
# OFFER -> RESPOND
for index in range(2):
action = Action({'subject': world.agents.keys()[index],'verb': 'offer',
'object': world.agents.keys()[1-index]})
if action['subject'] == free.name or model != 'powell':
tree = makeTree(setToConstantMatrix(phase,'respond'))
world.setDynamics(phase,action,tree)
# RESPOND -> REJECTION or ENGAGEMENT
for action in filterActions({'verb': 'reject offer'},free.actions | sylv.actions):
if model == 'powell':
tree = makeTree(setToConstantMatrix(phase,'rejection'))
elif model == 'slantchev':
tree = makeTree(setToConstantMatrix(phase,'engagement'))
world.setDynamics(phase,action,tree)
# accepting -> OFFER
for action in filterActions({'verb': 'accept offer'},free.actions | sylv.actions):
tree = makeTree(setToConstantMatrix(phase,'offer'))
world.setDynamics(phase,action,tree)
# attacking -> OFFER
for action in filterActions({'verb': 'attack'},free.actions | sylv.actions):
tree = makeTree(setToConstantMatrix(phase,'offer'))
world.setDynamics(phase,action,tree)
if action['subject'] == sylv.name or model == 'slantchev':
tree = makeTree(incrementMatrix(roundKey,1))
world.setDynamics(roundKey,action,tree)
if model == 'powell':
# REJECTION -> END
for atom in [freeNOP,freeBattle]:
tree = makeTree(setToConstantMatrix(phase,'end'))
world.setDynamics(phase,atom,tree)
# END -> OFFER
atom = Action({'subject': sylv.name,'verb': 'continue'})
tree = makeTree(setToConstantMatrix(phase,'offer'))
world.setDynamics(phase,atom,tree)
tree = makeTree(incrementMatrix(roundKey,1))
world.setDynamics(roundKey,atom,tree)
if not web:
# Relationship dynamics: attacking is bad for trust
atom = Action({'subject': sylv.name,'verb': 'attack','object': free.name})
key = binaryKey(free.name,sylv.name,'trusts')
tree = makeTree(approachMatrix(key,0.1,-1.))
world.setDynamics(key,atom,tree)
# Handcrafted policy for Freedonia
# free.setPolicy(makeTree({'if': equalRow('phase','respond'),
# # Accept an offer greater than 50
# True: {'if': thresholdRow(stateKey(free.name,'offered'),50),
# True: Action({'subject': free.name,'verb': 'accept offer','object': sylv.name}),
# False: Action({'subject': free.name,'verb': 'reject offer','object': sylv.name})},
# False: {'if': equalRow('phase','engagement'),
# # Attack during engagement phase
# True: Action({'subject': free.name,'verb': 'attack','object': sylv.name}),
# # Agent decides how what to do otherwise
# False: False}}))
# Mental models of enemy
# Example of creating a model with incorrect reward all at once (a version of Freedonia who cares about reaching agreement as well)
# sylv.addModel('false',R={goalSTroops: 10.,goalSTerritory: 1.,goalAgreement: 1.},
# rationality=1.,selection='distribution',parent=True)
# Example of creating a model with incorrect beliefs
sylv.addModel('false',rationality=10.,selection='distribution',parent=True)
key = stateKey(free.name,'position')
# Sylvania believes position to be fixed at 3
sylv.setBelief(key,3,'false')
# Freedonia is truly unsure about position (50% chance of being 7, 50% of being 3)
world.setModel(free.name,True)
free.setBelief(key,Distribution({7: 0.5,3: 0.5}),True)
# Observations about military position
tree = makeTree({'if': thresholdRow(key,1),
True: {'if': thresholdRow(key,9),
True: {'distribution': [(KeyedVector({key: 1}),0.9),
(KeyedVector({key: 1,CONSTANT: -1}),0.1)]},
False: {'distribution': [(KeyedVector({key: 1}),0.8),
(KeyedVector({key: 1,CONSTANT: -1}),0.1),
(KeyedVector({key: 1,CONSTANT: 1}),0.1)]}},
False: {'distribution': [(KeyedVector({key: 1}),0.9),
(KeyedVector({key: 1,CONSTANT: 1}),0.1)]}})
free.defineObservation(key,tree)
# Example of setting model parameters separately
sylv.addModel('true',parent=True)
sylv.setAttribute('rationality',10.,'true') # Override real agent's rationality with this value
sylv.setAttribute('selection','distribution','true')
world.setMentalModel(free.name,sylv.name,{'false': 0.9,'true': 0.1})
# Goal of fooling Sylvania
goalDeception = achieveFeatureValue(modelKey(sylv.name),sylv.model2index('false'))
return world
False: False
},
False: False
}))
# Parameters
me.setHorizon(6)
# me.setParameter('discount',0.9)
#me.setParameter('discount',0.9)
# Levels of belief
david.setRecursiveLevel(3)
stacy.setRecursiveLevel(3)
# Turn order: Uncomment the following if you want agents to act in parallel
#world.setOrder([{agts[0].name,agts[1].name}])
# Turn order: Uncomment the following if you want agents to act sequentially
world.setOrder([agts[0].name, agts[1].name])
# World state
world.defineState(None, 'agreement', bool)
world.setState(None, 'agreement', False)
world.defineState(None, 'scotchOffer', bool)
world.setState(None, 'scotchOffer', False)
world.defineState(None, 'tequilaOffer', bool)
world.setState(None, 'tequilaOffer', False)
world.defineState(None,
'round',
int,
description='The current round of the negotiation')
world.setState(None, 'round', 0)
world.addTermination(
False: {
'if': equalRow(my_dec, DEFECTED),
True: False,
False: True
}
}))
tree = makeTree(setToConstantMatrix(my_dec, COOPERATED))
world.setDynamics(my_dec, action, tree)
# defines payoff matrices (equal to both agents)
agent1.setReward(get_reward_tree(agent1, agents_dec[0], agents_dec[1]), 1)
agent2.setReward(get_reward_tree(agent2, agents_dec[1], agents_dec[0]), 1)
# define order
my_turn_order = [{agent1.name, agent2.name}]
world.setOrder(my_turn_order)
# add true mental model of the other to each agent
world.setMentalModel(agent1.name, agent2.name,
Distribution({agent2.get_true_model(): 1}))
world.setMentalModel(agent2.name, agent1.name,
Distribution({agent1.get_true_model(): 1}))
for h in range(MAX_HORIZON + 1):
logging.info('====================================')
logging.info(f'Horizon {h}')
# set horizon (also to the true model!) and reset decisions
for i in range(len(agents)):
agents[i].setHorizon(h)
agents[i].setHorizon(h, agents[i].get_true_model())
# define agents' actions (stay 0, left -1 and right +1)
action = agent.addAction({'verb': 'move', 'action': 'nowhere'})
tree = makeTree(setToFeatureMatrix(pos, pos))
world.setDynamics(pos, action, tree)
action = agent.addAction({'verb': 'move', 'action': 'left'})
tree = makeTree(incrementMatrix(pos, -1))
world.setDynamics(pos, action, tree)
action = agent.addAction({'verb': 'move', 'action': 'right'})
tree = makeTree(incrementMatrix(pos, 1))
world.setDynamics(pos, action, tree)
# define rewards (maximize position, i.e., always go right)
agent.setReward(maximizeFeature(pos, agent.name), 1)
# set order
world.setOrder([agent.name])
# agent has initial beliefs about its position, which will be updated after executing actions
agent.omega = {actionKey(agent.name)} # todo should not need this
agent.setBelief(pos, Distribution({10: 0.5, 12: 0.5}))
# agent.setBelief(pos, 10, get_true_model_name(agent))
print('====================================')
print('Initial beliefs:')
world.printBeliefs(agent.name)
for i in range(MAX_STEPS):
print('====================================')
print('Current pos: {0}'.format(world.getValue(pos)))
# decision: left, right or no-move?
def scenarioCreationUseCase(enemy='Sylvania',
model='powell',
web=False,
fCollapse=None,
sCollapse=None,
maxRounds=15):
"""
An example of how to create a scenario
@param enemy: the name of the agent-controlled side, i.e., Freedonia's opponent (default: Sylvania)
@type enemy: str
@param model: which model do we use (default is "powell")
@type model: powell or slantchev
@param web: if C{True}, then create the web-based experiment scenario (default: C{False})
@type web: bool
@param fCollapse: the probability that Freedonia collapses (under powell, default: 0.1) or loses battle (under slantchev, default: 0.7)
@type fCollapse: float
@param sCollapse: the probability that Sylvania collapses, under powell (default: 0.1)
@type sCollapse: float
@param maxRounds: the maximum number of game rounds (default: 15)
@type maxRounds: int
@return: the scenario created
@rtype: L{World}
"""
# Handle defaults for battle probabilities, under each model
posLo = 0
posHi = 10
if fCollapse is None:
if model == 'powell':
fCollapse = 0.1
elif model == 'slantchev':
fCollapse = 0.7
if sCollapse is None:
sCollapse = 0.1
# Create scenario
world = World()
# Agents
free = Agent('Freedonia')
world.addAgent(free)
sylv = Agent(enemy)
world.addAgent(sylv)
# User state
world.defineState(free.name,
'troops',
int,
lo=0,
hi=50000,
description='Number of troops you have left')
free.setState('troops', 40000)
world.defineState(
free.name,
'territory',
int,
lo=0,
hi=100,
description='Percentage of disputed territory owned by you')
free.setState('territory', 15)
world.defineState(free.name,
'cost',
int,
lo=0,
hi=50000,
description='Number of troops %s loses in an attack' %
(free.name))
free.setState('cost', 2000)
world.defineState(
free.name,
'position',
int,
lo=posLo,
hi=posHi,
description='Current status of war (%d=%s is winner, %d=you are winner)'
% (posLo, sylv.name, posHi))
free.setState('position', 5)
world.defineState(
free.name,
'offered',
int,
lo=0,
hi=100,
description=
'Percentage of disputed territory that %s last offered to you' %
(sylv.name))
free.setState('offered', 0)
if model == 'slantchev':
# Compute new value for territory only *after* computing new value for position
world.addDependency(stateKey(free.name, 'territory'),
stateKey(free.name, 'position'))
# Agent state
world.defineState(sylv.name,
'troops',
int,
lo=0,
hi=500000,
description='Number of troops %s has left' % (sylv.name))
sylv.setState('troops', 30000)
world.defineState(sylv.name,
'cost',
int,
lo=0,
hi=50000,
description='Number of troops %s loses in an attack' %
(sylv.name))
sylv.setState('cost', 2000)
world.defineState(
sylv.name,
'offered',
int,
lo=0,
hi=100,
description=
'Percentage of disputed territory that %s last offered to %s' %
(free.name, sylv.name))
sylv.setState('offered', 0)
# World state
world.defineState(None,
'treaty',
bool,
description='Have the two sides reached an agreement?')
world.setState(None, 'treaty', False)
# Stage of negotiation, illustrating the use of an enumerated state feature
world.defineState(
None,
'phase',
list, ['offer', 'respond', 'rejection', 'end', 'paused', 'engagement'],
description='The current stage of the negotiation game')
world.setState(None, 'phase', 'paused')
# Game model, static descriptor
world.defineState(None,
'model',
list, ['powell', 'slantchev'],
description='The model underlying the negotiation game')
world.setState(None, 'model', model)
# Round of negotiation
world.defineState(None,
'round',
int,
description='The current round of the negotiation')
world.setState(None, 'round', 0)
if not web:
# Relationship value
key = world.defineRelation(free.name, sylv.name, 'trusts')
world.setFeature(key, 0.)
# Game over if there is a treaty
world.addTermination(
makeTree({
'if': trueRow(stateKey(None, 'treaty')),
True: True,
False: False
}))
# Game over if Freedonia has no territory
world.addTermination(
makeTree({
'if': thresholdRow(stateKey(free.name, 'territory'), 1),
True: False,
False: True
}))
# Game over if Freedonia has all the territory
world.addTermination(
makeTree({
'if': thresholdRow(stateKey(free.name, 'territory'), 99),
True: True,
False: False
}))
# Game over if number of rounds exceeds limit
world.addTermination(
makeTree({
'if': thresholdRow(stateKey(None, 'round'), maxRounds),
True: True,
False: False
}))
# Turn order: Uncomment the following if you want agents to act in parallel
# world.setOrder([set(world.agents.keys())])
# Turn order: Uncomment the following if you want agents to act sequentially
world.setOrder([free.name, sylv.name])
# User actions
freeBattle = free.addAction({'verb': 'attack', 'object': sylv.name})
for amount in range(20, 100, 20):
free.addAction({
'verb': 'offer',
'object': sylv.name,
'amount': amount
})
if model == 'powell':
# Powell has null stages
freeNOP = free.addAction({'verb': 'continue'})
elif model == 'slantchev':
# Slantchev has both sides receiving offers
free.addAction({'verb': 'accept offer', 'object': sylv.name})
free.addAction({'verb': 'reject offer', 'object': sylv.name})
# Agent actions
sylvBattle = sylv.addAction({'verb': 'attack', 'object': free.name})
sylvAccept = sylv.addAction({'verb': 'accept offer', 'object': free.name})
sylvReject = sylv.addAction({'verb': 'reject offer', 'object': free.name})
if model == 'powell':
# Powell has null stages
sylvNOP = sylv.addAction({'verb': 'continue'})
elif model == 'slantchev':
# Slantchev has both sides making offers
for amount in range(10, 100, 10):
sylv.addAction({
'verb': 'offer',
'object': free.name,
'amount': amount
})
# Restrictions on when actions are legal, based on phase of game
for action in filterActions({'verb': 'offer'},
free.actions | sylv.actions):
agent = world.agents[action['subject']]
agent.setLegal(
action,
makeTree({
'if': equalRow(stateKey(None, 'phase'), 'offer'),
True: True, # Offers are legal in the offer phase
False: False
})) # Offers are illegal in all other phases
if model == 'powell':
# Powell has a special rejection phase
for action in [freeNOP, freeBattle]:
free.setLegal(
action,
makeTree({
'if': equalRow(stateKey(None, 'phase'), 'rejection'),
True:
True, # Attacking and doing nothing are legal only in rejection phase
False: False
})
) # Attacking and doing nothing are illegal in all other phases
# Once offered, agent can respond
if model == 'powell':
# Under Powell, only Sylvania has to respond, and it can attack
responses = [sylvBattle, sylvAccept, sylvReject]
elif model == 'slantchev':
# Under Slantchev, only accept/reject
responses = filterActions({'verb': 'accept offer'},
free.actions | sylv.actions)
responses += filterActions({'verb': 'reject offer'},
free.actions | sylv.actions)
for action in responses:
agent = world.agents[action['subject']]
agent.setLegal(
action,
makeTree({
'if': equalRow(stateKey(None, 'phase'), 'respond'),
True: True, # Offeree must act in the response phase
False: False
})) # Offeree cannot act in any other phase
if model == 'powell':
# NOP is legal in exactly opposite situations to all other actions
sylv.setLegal(
sylvNOP,
makeTree({
'if': equalRow(stateKey(None, 'phase'), 'end'),
True:
True, # Sylvania does not do anything in the null phase after Freedonia responds to rejection
False: False
})) # Sylvania must act in its other phases
if model == 'slantchev':
# Attacking legal only under engagement phase
for action in filterActions({'verb': 'attack'},
free.actions | sylv.actions):
agent = world.agents[action['subject']]
agent.setLegal(
action,
makeTree({
'if': equalRow(stateKey(None, 'phase'), 'engagement'),
True: True, # Attacking legal only in engagement
False: False
})) # Attacking legal every other phase
# Goals for Freedonia
goalFTroops = maximizeFeature(stateKey(free.name, 'troops'))
free.setReward(goalFTroops, 1.)
goalFTerritory = maximizeFeature(stateKey(free.name, 'territory'))
free.setReward(goalFTerritory, 1.)
# Goals for Sylvania
goalSTroops = maximizeFeature(stateKey(sylv.name, 'troops'))
sylv.setReward(goalSTroops, 1.)
goalSTerritory = minimizeFeature(stateKey(free.name, 'territory'))
sylv.setReward(goalSTerritory, 1.)
# Possible goals applicable to both
goalAgreement = maximizeFeature(stateKey(None, 'treaty'))
# Silly goal, provided as an example of an achievement goal
goalAchieve = achieveFeatureValue(stateKey(None, 'phase'), 'respond')
# Horizons
if model == 'powell':
free.setAttribute('horizon', 4)
sylv.setAttribute('horizon', 4)
elif model == 'slantchev':
free.setAttribute('horizon', 6)
sylv.setAttribute('horizon', 6)
# Discount factors
free.setAttribute('discount', -1)
sylv.setAttribute('discount', -1)
# Levels of belief
free.setRecursiveLevel(2)
sylv.setRecursiveLevel(2)
# Dynamics of battle
freeTroops = stateKey(free.name, 'troops')
freeTerr = stateKey(free.name, 'territory')
sylvTroops = stateKey(sylv.name, 'troops')
# Effect of fighting
for action in filterActions({'verb': 'attack'},
free.actions | sylv.actions):
# Effect on troops (cost of battle)
tree = makeTree(
addFeatureMatrix(freeTroops, stateKey(free.name, 'cost'), -1.))
world.setDynamics(freeTroops, action, tree, enforceMin=not web)
tree = makeTree(
addFeatureMatrix(sylvTroops, stateKey(sylv.name, 'cost'), -1.))
world.setDynamics(sylvTroops, action, tree, enforceMin=not web)
if model == 'powell':
# Effect on territory (probability of collapse)
tree = makeTree({
'distribution': [
(
{
'distribution': [
(setToConstantMatrix(freeTerr,
100), 1. - fCollapse
), # Sylvania collapses, Freedonia does not
(noChangeMatrix(freeTerr), fCollapse)
]
}, # Both collapse
sCollapse),
(
{
'distribution': [
(setToConstantMatrix(freeTerr, 0), fCollapse
), # Freedonia collapses, Sylvania does not
(noChangeMatrix(freeTerr), 1. - fCollapse)
]
}, # Neither collapses
1. - sCollapse)
]
})
world.setDynamics(freeTerr, action, tree)
elif model == 'slantchev':
# Effect on position
pos = stateKey(free.name, 'position')
tree = makeTree({
'distribution': [
(incrementMatrix(pos, 1),
1. - fCollapse), # Freedonia wins battle
(incrementMatrix(pos, -1), fCollapse)
]
}) # Freedonia loses battle
world.setDynamics(pos, action, tree)
# Effect on territory
tree = makeTree({
'if': thresholdRow(pos, posHi - .5),
True: setToConstantMatrix(freeTerr, 100), # Freedonia won
False: {
'if': thresholdRow(pos, posLo + .5),
True: noChangeMatrix(freeTerr),
False: setToConstantMatrix(freeTerr, 0)
}
}) # Freedonia lost
world.setDynamics(freeTerr, action, tree)
# Dynamics of offers
for index in range(2):
atom = Action({
'subject': world.agents.keys()[index],
'verb': 'offer',
'object': world.agents.keys()[1 - index]
})
if atom['subject'] == free.name or model != 'powell':
offer = stateKey(atom['object'], 'offered')
amount = actionKey('amount')
tree = makeTree({
'if': trueRow(stateKey(None, 'treaty')),
True: noChangeMatrix(offer),
False: setToConstantMatrix(offer, amount)
})
world.setDynamics(offer, atom, tree, enforceMax=not web)
# Dynamics of treaties
for action in filterActions({'verb': 'accept offer'},
free.actions | sylv.actions):
# Accepting an offer means that there is now a treaty
key = stateKey(None, 'treaty')
tree = makeTree(setTrueMatrix(key))
world.setDynamics(key, action, tree)
# Accepting offer sets territory
offer = stateKey(action['subject'], 'offered')
territory = stateKey(free.name, 'territory')
if action['subject'] == free.name:
# Freedonia accepts sets territory to last offer
tree = makeTree(setToFeatureMatrix(territory, offer))
world.setDynamics(freeTerr, action, tree)
else:
# Sylvania accepts sets territory to 1-last offer
tree = makeTree(
setToFeatureMatrix(territory, offer, pct=-1., shift=100.))
world.setDynamics(freeTerr, action, tree)
# Dynamics of phase
phase = stateKey(None, 'phase')
roundKey = stateKey(None, 'round')
# OFFER -> RESPOND
for index in range(2):
action = Action({
'subject': world.agents.keys()[index],
'verb': 'offer',
'object': world.agents.keys()[1 - index]
})
if action['subject'] == free.name or model != 'powell':
tree = makeTree(setToConstantMatrix(phase, 'respond'))
world.setDynamics(phase, action, tree)
# RESPOND -> REJECTION or ENGAGEMENT
for action in filterActions({'verb': 'reject offer'},
free.actions | sylv.actions):
if model == 'powell':
tree = makeTree(setToConstantMatrix(phase, 'rejection'))
elif model == 'slantchev':
tree = makeTree(setToConstantMatrix(phase, 'engagement'))
world.setDynamics(phase, action, tree)
# accepting -> OFFER
for action in filterActions({'verb': 'accept offer'},
free.actions | sylv.actions):
tree = makeTree(setToConstantMatrix(phase, 'offer'))
world.setDynamics(phase, action, tree)
# attacking -> OFFER
for action in filterActions({'verb': 'attack'},
free.actions | sylv.actions):
tree = makeTree(setToConstantMatrix(phase, 'offer'))
world.setDynamics(phase, action, tree)
if action['subject'] == sylv.name or model == 'slantchev':
tree = makeTree(incrementMatrix(roundKey, 1))
world.setDynamics(roundKey, action, tree)
if model == 'powell':
# REJECTION -> END
for atom in [freeNOP, freeBattle]:
tree = makeTree(setToConstantMatrix(phase, 'end'))
world.setDynamics(phase, atom, tree)
# END -> OFFER
atom = Action({'subject': sylv.name, 'verb': 'continue'})
tree = makeTree(setToConstantMatrix(phase, 'offer'))
world.setDynamics(phase, atom, tree)
tree = makeTree(incrementMatrix(roundKey, 1))
world.setDynamics(roundKey, atom, tree)
if not web:
# Relationship dynamics: attacking is bad for trust
atom = Action({
'subject': sylv.name,
'verb': 'attack',
'object': free.name
})
key = binaryKey(free.name, sylv.name, 'trusts')
tree = makeTree(approachMatrix(key, 0.1, -1.))
world.setDynamics(key, atom, tree)
# Handcrafted policy for Freedonia
# free.setPolicy(makeTree({'if': equalRow('phase','respond'),
# # Accept an offer greater than 50
# True: {'if': thresholdRow(stateKey(free.name,'offered'),50),
# True: Action({'subject': free.name,'verb': 'accept offer','object': sylv.name}),
# False: Action({'subject': free.name,'verb': 'reject offer','object': sylv.name})},
# False: {'if': equalRow('phase','engagement'),
# # Attack during engagement phase
# True: Action({'subject': free.name,'verb': 'attack','object': sylv.name}),
# # Agent decides how what to do otherwise
# False: False}}))
# Mental models of enemy
# Example of creating a model with incorrect reward all at once (a version of Freedonia who cares about reaching agreement as well)
# sylv.addModel('false',R={goalSTroops: 10.,goalSTerritory: 1.,goalAgreement: 1.},
# rationality=1.,selection='distribution',parent=True)
# Example of creating a model with incorrect beliefs
sylv.addModel('false',
rationality=10.,
selection='distribution',
parent=True)
key = stateKey(free.name, 'position')
# Sylvania believes position to be fixed at 3
sylv.setBelief(key, 3, 'false')
# Freedonia is truly unsure about position (50% chance of being 7, 50% of being 3)
world.setModel(free.name, True)
free.setBelief(key, Distribution({7: 0.5, 3: 0.5}), True)
# Observations about military position
tree = makeTree({
'if': thresholdRow(key, 1),
True: {
'if': thresholdRow(key, 9),
True: {
'distribution': [(KeyedVector({key: 1}), 0.9),
(KeyedVector({
key: 1,
CONSTANT: -1
}), 0.1)]
},
False: {
'distribution': [(KeyedVector({key: 1}), 0.8),
(KeyedVector({
key: 1,
CONSTANT: -1
}), 0.1),
(KeyedVector({
key: 1,
CONSTANT: 1
}), 0.1)]
}
},
False: {
'distribution': [(KeyedVector({key: 1}), 0.9),
(KeyedVector({
key: 1,
CONSTANT: 1
}), 0.1)]
}
})
free.defineObservation(key, tree)
# Example of setting model parameters separately
sylv.addModel('true', parent=True)
sylv.setAttribute(
'rationality', 10.,
'true') # Override real agent's rationality with this value
sylv.setAttribute('selection', 'distribution', 'true')
world.setMentalModel(free.name, sylv.name, {'false': 0.9, 'true': 0.1})
# Goal of fooling Sylvania
goalDeception = achieveFeatureValue(modelKey(sylv.name),
sylv.model2index('false'))
return world
# Parameters
me.setHorizon(1)
# me.setParameter('discount',0.9)
me.setParameter('discount',0.2)
# Turn order: Uncomment the following if you want agents to act in parallel
actors = world.agents.keys()
# actors = set(actors)
# print actors
# actors.discard('door')
# actors.discard('fire')
# world.setOrder([actors])
actors.remove('door')
actors.remove('fire')
world.setOrder([set(actors)])
print actors
for agt in actors:
atom = Action({'subject': agt,'verb': 'runAway', 'object':'fire'})
tree = makeTree(incrementMatrix(stateKey(atom['subject'],'fire_dist'),.1))
world.setDynamics(agt,'fire_dist',atom,tree)
atom = Action({'subject': agt,'verb': 'runTowards', 'object':'door'})
tree = makeTree(incrementMatrix(stateKey(atom['subject'],'door_dist'),-.1))
world.setDynamics(agt,'door_dist',atom,tree)
atom = Action({'subject': agt,'verb': 'runClosest'})
tree = makeTree(incrementMatrix(stateKey(atom['subject'],'closest_dist'),-.1))
world.setDynamics(agt,'door_dist',atom,tree)
class Negotiate:
def __init__(self, turnOrder):
self.maxRounds = 8
self.world = World()
totals = {'apple': 1, 'pear': 2}
batna_prePref = totals['apple'] + totals['pear']
stacy = Agent('Stacy')
david = Agent('David')
agts = [stacy, david]
# Player state, actions and parameters common to both players
for i in range(2):
me = agts[i]
other = agts[1 - i]
self.world.addAgent(me)
# State
self.world.defineState(me.name,
'appleOwned',
int,
lo=0,
hi=totals['apple'])
me.setState('appleOwned', 0)
self.world.defineState(me.name,
'appleOffered',
int,
lo=0,
hi=totals['apple'])
me.setState('appleOffered', 0)
self.world.defineState(me.name,
'pearOwned',
int,
lo=0,
hi=totals['pear'])
me.setState('pearOwned', 0)
self.world.defineState(me.name,
'pearOffered',
int,
lo=0,
hi=totals['pear'])
me.setState('pearOffered', 0)
self.world.defineState(me.name, 'Batna', int, lo=0, hi=10)
me.setState('Batna', batna_prePref)
self.world.defineState(me.name, 'BatnaOwned', int, lo=0, hi=10)
me.setState('BatnaOwned', 0)
self.world.defineState(me.name, 'agree', bool)
me.setState('agree', False)
# Actions
me.addAction({'verb': 'do nothing'})
for amt in range(totals['apple'] + 1):
tmp = me.addAction({
'verb': 'offerApple',
'object': other.name,
'amount': amt
})
me.setLegal(
tmp,
makeTree({
'if': trueRow(stateKey(None, 'agreement')),
False: {
'if': trueRow(stateKey(None,
'rejectedNegotiation')),
True: False,
False: True
},
True: False
}))
for amt in range(totals['pear'] + 1):
tmp = me.addAction({
'verb': 'offerPear',
'object': other.name,
'amount': amt
})
me.setLegal(
tmp,
makeTree({
'if': trueRow(stateKey(None, 'agreement')),
False: {
'if': trueRow(stateKey(None,
'rejectedNegotiation')),
True: False,
False: True
},
True: False
}))
meReject = me.addAction({
'verb': 'rejectNegotiation',
'object': other.name
})
me.setLegal(
meReject,
makeTree({
'if': trueRow(stateKey(None, 'agreement')),
False: {
'if': trueRow(stateKey(None, 'rejectedNegotiation')),
True: False,
False: True
},
True: False
}))
meAccept = me.addAction({
'verb': 'accept offer',
'object': other.name
})
me.setLegal(
meAccept,
makeTree({
'if': trueRow(stateKey(None, 'appleOffer')),
True: {
'if': trueRow(stateKey(None, 'pearOffer')),
True: {
'if': trueRow(stateKey(None, 'agreement')),
False: {
'if':
trueRow(stateKey(None, 'rejectedNegotiation')),
True:
False,
False:
True
},
True: False
},
False: False
},
False: False
}))
# Parameters
me.setHorizon(6)
me.setParameter('discount', 0.9)
# Levels of belief
david.setRecursiveLevel(3)
stacy.setRecursiveLevel(3)
# Turn order: Uncomment the following if you want agents to act in parallel
# world.setOrder([{agts[0].name,agts[1].name}])
# Turn order: Uncomment the following if you want agents to act sequentially
self.world.setOrder(turnOrder)
# World state
self.world.defineState(None, 'agreement', bool)
self.world.setState(None, 'agreement', False)
self.world.defineState(None, 'appleOffer', bool)
self.world.setState(None, 'appleOffer', False)
self.world.defineState(None, 'pearOffer', bool)
self.world.setState(None, 'pearOffer', False)
self.world.defineState(
None,
'round',
int,
description='The current round of the negotiation')
self.world.setState(None, 'round', 0)
self.world.defineState(
None,
'rejectedNegotiation',
bool,
description='Have one of the players walked out?')
self.world.setState(None, 'rejectedNegotiation', False)
# dont terminate so agent sees benefit of early agreement
# world.addTermination(makeTree({'if': trueRow(stateKey(None,'agreement')),
# True: True,
# False: False}))
# world.addTermination(makeTree({'if': trueRow(stateKey(None,'rejectedNegotiation')),
# True: True,
# False: False}))
self.world.addTermination(
makeTree({
'if':
thresholdRow(stateKey(None, 'round'), self.maxRounds),
True:
True,
False:
False
}))
# Dynamics of offers
agents = [david.name, stacy.name]
for i in range(2):
for fruit in ['apple', 'pear']:
atom = Action({
'subject': agents[i],
'verb': 'offer%s' % (fruit.capitalize()),
'object': agents[1 - i]
})
parties = [atom['subject'], atom['object']]
for j in range(2):
# Set offer amount
offer = stateKey(parties[j], '%sOffered' % (fruit))
amount = actionKey('amount') if j == 1 else '%d-%s' % (
totals[fruit], actionKey('amount'))
tree = makeTree(setToConstantMatrix(offer, amount))
self.world.setDynamics(parties[j], '%sOffered' % (fruit),
atom, tree)
# reset agree flags whenever an offer is made
agreeFlag = stateKey(parties[j], 'agree')
tree = makeTree(setFalseMatrix(agreeFlag))
self.world.setDynamics(parties[j], 'agree', atom, tree)
# Offers set offer flag in world state
tree = makeTree(
setTrueMatrix(stateKey(None, '%sOffer' % (fruit))))
self.world.setDynamics(None, '%sOffer' % (fruit), atom, tree)
# agents = [david.name,stacy.name]
# Dynamics of agreements
for i in range(2):
atom = Action({
'subject': agents[i],
'verb': 'accept offer',
'object': agents[1 - i]
})
# accept offer sets accept
tree = makeTree(setTrueMatrix(stateKey(atom['subject'], 'agree')))
self.world.setDynamics(atom['subject'], 'agree', atom, tree)
# accept offer sets agreement if object has accepted
tree = makeTree({
'if': trueRow(stateKey(atom['object'], 'agree')),
True: setTrueMatrix(stateKey(None, 'agreement')),
False: noChangeMatrix(stateKey(None, 'agreement'))
})
self.world.setDynamics(None, 'agreement', atom, tree)
# Accepting offer sets ownership
parties = [atom['subject'], atom['object']]
for fruit in ['apple', 'pear']:
# atom = Action({'subject': agents[i],'verb': 'accept offer', 'object': agents[1-i]})
for j in range(2):
offer = stateKey(parties[j], '%sOffered' % (fruit))
owned = stateKey(parties[j], '%sOwned' % (fruit))
tree = makeTree({
'if':
trueRow(stateKey(atom['object'], 'agree')),
False:
noChangeMatrix(owned),
True:
setToFeatureMatrix(owned, offer)
})
self.world.setDynamics(parties[j], '%sOwned' % (fruit),
atom, tree)
# rejecting give us batna and ends negotiation
atom = Action({
'subject': agents[i],
'verb': 'rejectNegotiation',
'object': agents[1 - i]
})
tree = makeTree(
setToFeatureMatrix(stateKey(atom['subject'], 'BatnaOwned'),
stateKey(atom['subject'], 'Batna')))
self.world.setDynamics(atom['subject'], 'BatnaOwned', atom, tree)
tree = makeTree(
setToFeatureMatrix(stateKey(atom['object'], 'BatnaOwned'),
stateKey(atom['object'], 'Batna')))
self.world.setDynamics(atom['object'], 'BatnaOwned', atom, tree)
tree = makeTree(
setTrueMatrix(stateKey(None, 'rejectedNegotiation')))
self.world.setDynamics(None, 'rejectedNegotiation', atom, tree)
for action in stacy.actions | david.actions:
tree = makeTree(incrementMatrix(stateKey(None, 'round'), 1))
self.world.setDynamics(None, 'round', action, tree)
for agent in self.world.agents.values():
agent.addModel('pearLover', R={}, level=2, rationality=0.01)
agent.addModel('appleLover', R={}, level=2, rationality=0.01)
def modeltest(self, trueModels, davidBeliefAboutStacy,
stacyBeliefAboutDavid, strongerBelief):
for agent in self.world.agents.values():
for model in agent.models.keys():
if model is True:
name = trueModels[agent.name]
else:
name = model
if name == 'appleLover':
agent.setReward(
maximizeFeature(stateKey(agent.name, 'appleOwned')),
4.0, model)
agent.setReward(
maximizeFeature(stateKey(agent.name, 'pearOwned')),
1.0, model)
agent.setReward(
maximizeFeature(stateKey(agent.name, 'BatnaOwned')),
0.1, model)
elif name == 'pearLover':
agent.setReward(
maximizeFeature(stateKey(agent.name, 'appleOwned')),
1.0, model)
agent.setReward(
maximizeFeature(stateKey(agent.name, 'pearOwned')),
4.0, model)
agent.setReward(
maximizeFeature(stateKey(agent.name, 'BatnaOwned')),
0.1, model)
weakBelief = 1.0 - strongerBelief
belief = {'pearLover': weakBelief, 'appleLover': weakBelief}
belief[davidBeliefAboutStacy] = strongerBelief
self.world.setMentalModel('David', 'Stacy', belief)
belief = {'pearLover': weakBelief, 'appleLover': weakBelief}
belief[stacyBeliefAboutDavid] = strongerBelief
self.world.setMentalModel('Stacy', 'David', belief)
def runit(self, Msg):
print Msg
for t in range(self.maxRounds + 1):
self.world.explain(self.world.step(), level=1)
# print self.world.step()
self.world.state.select()
# self.world.printState()
if self.world.terminated():
break
action = agent.addAction({'verb': '', 'action': 'go right'})
tree = makeTree(setToConstantMatrix(side, WENT_RIGHT))
world.setDynamics(side, action, tree)
rights.append(action)
# create a new model for the agent
agent.addModel(get_fake_model_name(agent),
parent=agent.get_true_model())
# defines payoff matrices
agent1.setReward(get_reward_tree(agent1, sides[0], sides[1]), 1)
agent2.setReward(get_reward_tree(agent2, sides[1], sides[0]), 1)
# define order
world.setOrder([{agent1.name, agent2.name}])
# add mental model of the other for each agent
world.setMentalModel(agent1.name, agent2.name,
Distribution({get_fake_model_name(agent2): 1}))
world.setMentalModel(agent2.name, agent1.name,
Distribution({get_fake_model_name(agent1): 1}))
# 'hides' right actions from models by setting them illegal
# (therefore agents should always choose right because they think the other will choose left)
set_illegal_action(agent1, rights[0], [get_fake_model_name(agent1)])
set_illegal_action(agent2, rights[1], [get_fake_model_name(agent2)])
# # ** unnecessary / just for illustration **: set left actions legal for both the agents and their models
# set_legal_action(agent1, lefts[0], [agent1.get_true_model(), get_fake_model_name(agent1)])
# set_legal_action(agent2, lefts[1], [agent2.get_true_model(), get_fake_model_name(agent2)])