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()
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
True: setToFeatureMatrix(owned, offer)
})
world.setDynamics(atom['object'], '%ssOwned' % (fruit), atom, tree)
offer = stateKey(atom['subject'], '%ssOffered' % (fruit))
owned = stateKey(atom['subject'], '%ssOwned' % (fruit))
tree = makeTree({
'if': trueRow('agreement'), # this test shouldn't be necessary
False: setToFeatureMatrix(owned, offer),
True: setToFeatureMatrix(owned, offer)
})
world.setDynamics(atom['subject'], '%ssOwned' % (fruit), atom,
tree)
# Save scenario to compressed XML file
world.save('default.psy')
# Create configuration file
# config = SafeConfigParser()
# f = open('default.cfg','w')
# config.write(f)
# f.close()
# Test saved scenario
world = World('default.psy')
world.printState()
for t in range(7):
world.explain(world.step())
world.state.select()
world.printState()
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
# 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())
world.setFeature(agents_dec[i], NOT_DECIDED, recurse=True)
for t in range(NUM_STEPS):
# decision per step (1 per agent): cooperate or defect?
logging.info('---------------------')
logging.info(f'Step {t}')
step = world.step()
for i in range(len(agents)):
logging.info(
f'{agents[i].name}: {world.getFeature(agents_dec[i], unique=True)}'
)
# Stacy's models of David
david.addModel('pearLover',R={appleGoalD: 1.0,pearGoalD: 4.0,BatnaGoalD: 6.0},level=2,rationality=0.01)
david.addModel('appleLover',R={appleGoalD: 4.0,pearGoalD: 1.0,BatnaGoalD: 0.1},level=2,rationality=0.01)
world.setMentalModel(stacy.name,david.name,{'pearLover': 0.5,'appleLover': 0.5})
# Save scenario to compressed XML file
world.save('default.psy')
# Create configuration file
# config = SafeConfigParser()
# f = open('default.cfg','w')
# config.write(f)
# f.close()
# Test saved scenario
world = World('default.psy')
# world.printState()
for t in range(maxRounds + 1):
world.explain(world.step())
# print world.step()
world.state.select()
world.printState()
if 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
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
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
# 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()
after = world.getValue(feat)
print('{:.3f}\t-> {}'.format(before, after))
values_original.append(before)
values_discrete.append(after)
# calculates RMSE
rmse = np.sqrt(np.mean((np.array(values_discrete) - values_original)**2))
print('\nRMSE: {:.3f}'.format(rmse))
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