def __init__(self, planlib, goal=None):
""" Assigns variables for this class, I'm assuming here that planlib is a set of Actions (from the planlib module)"""
super(basic_norm_detector,self).__init__(planlib)
self.reinitialise()
self.past_observations = [] # Make sure this is not in reinitialise, since this will mess up with the #learn_norms algorithm below
if(goal == None):
self.goal=Goal(start_nodes(planlib).pop(), goal_nodes(planlib).pop())
else:
self.goal = goal
def test():
goal = Goal('a', 'd')
actions = set([
Action(['a', 'b']),
Action(['b', 'e']),
Action(['b', 'c']),
Action(['b', 'd']),
Action(['a', 'f']),
Action(['a', 'c', 'e']),
Action(['e', 'd'])
])
observation1 = ['a', 'c', 'e', 'd']
observation2 = ['a', 'b', 'd', '!']
nodes = {node for action in actions for node in action.path}
successors = defaultdict(set)
for action in actions:
for i, node in enumerate(action.path[0:-1]):
successors[node].add(action.path[i + 1])
conditional_norms = [ (context, modality, node) for context in nodes for node in nodes \
for modality in 'eventually', 'never' ]
conditional_norms += [ (context, modality, node) for context in nodes for node in nodes \
for modality in 'next', 'not next' \
if node in successors[context] ]
unconditional_norms = [(modality, node) for node in nodes
for modality in 'eventually', 'never']
poss_norms = unconditional_norms + conditional_norms
hypotheses = dict.fromkeys(poss_norms, 0.05)
hypotheses[None] = 1 # Set prior odds ratio for hypothesis None
print "Goal: ", goal
print "Actions: ", actions
print "Norm hypotheses (with prior odds ratios): ", hypotheses
suite = NormSuite(goal, hypotheses, actions)
print "Plans: ", suite.plan_paths
print "Updating odds ratios after observing ", observation1
suite.UpdateOddsRatioVsNoNorm(observation1)
print "The posterior odds ratios are:"
suite.Print()
print "Updating odds ratios after observing ", observation2
suite.UpdateOddsRatioVsNoNorm(observation2)
print "The posterior odds ratios are:"
suite.Print()
f = open('graph.dot', 'w')
f.write('digraph {\n')
for a in suite.actions:
for i in range(len(a.path) - 1):
f.write(a.path[i] + ' -> ' + a.path[i + 1] + '\n')
f.write("}\n")
f.close()
def gen_scenario_large(self, prob_non_compliance=0.01, prob_viol_detection=0.99, \
prob_sanctioning=0.99, prob_random_punishment=0.01):
planlib = dot_to_plan_library('large-planlib.dot')
goal = Goal(
list(start_nodes(planlib))[0],
list(goal_nodes(planlib))[0])
norms = self.nb.parse_norms('large-norms.txt')
scenario = Scenario(norms, planlib, goal, prob_non_compliance, prob_viol_detection, \
prob_sanctioning, prob_random_punishment)
return scenario
def __init__(self,planlib, goal=None):
super(threshold_norm_detector,self).__init__(planlib)
self.planlib = planlib
self.ot = 0.5 # Threshold for obligations
self.ft = 0.5 # Threshold for prohibitions
self.reinitialise()
self.past_observations = []
self.goal = goal
if(goal == None):
self.goal=Goal(start_nodes(planlib).pop(), goal_nodes(planlib).pop())
else:
self.goal = goal
def setUp(self):
self.goal = Goal('a', 'd')
self.planlib = set([
Action(['a', 'b']),
Action(['b', 'e']),
Action(['b', 'c']),
Action(['b', 'd']),
Action(['a', 'f']),
Action(['a', 'c', 'e']),
Action(['e', 'd'])
])
self.observation1 = ['a', 'c', 'e', 'd']
self.observation2 = ['a', 'b', 'd']
self.observation3 = ['a', 'b', 'e', 'd']
self.norm_detector = norm_detector(self.planlib)
def gen_scenario_2(self, prob_non_compliance=0.01, prob_viol_detection=0.99, \
prob_sanctioning=0.99, prob_random_punishment=0.01):
"""Larger but acyclic graph, allows multiple goals"""
goal = Goal("a", "y")
planlib = set([
Action(["a", "0"]),
Action(["0", "y"]),
Action(["0", "j"]),
Action(["a", "w"]),
Action(["a", "l"]),
Action(["a", "e"]),
Action(["a", "s"]),
Action(["a", "d"]),
Action(["a", "o"]),
Action(["b", "q"]),
Action(["c", "z"]),
Action(["c", "f"]),
Action(["c", "n"]),
Action(["c", "g"]),
Action(["d", "h"]),
Action(["d", "r"]),
Action(["d", "t"]),
Action(["d", "z"]),
Action(["e", "s"]),
Action(["e", "b"]),
Action(["f", "i"]),
Action(["f", "2"]),
Action(["f", "u"]),
Action(["g", "k"]),
Action(["h", "1"]),
Action(["h", "v"]),
Action(["j", "t"]),
Action(["j", "3"]),
Action(["k", "x"]),
Action(["l", "s"]),
Action(["m", "y"]),
Action(["n", "p"]),
Action(["r", "1"]),
Action(["s", "m"]),
Action(["o", "c"]),
Action(["q", "y"])
])
norms = set([('a', 'never', 'l')])
scenario = Scenario(norms, planlib, goal, prob_non_compliance, prob_viol_detection, \
prob_sanctioning, prob_random_punishment)
return scenario
def gen_scenario_1(self, prob_non_compliance=0.01, prob_viol_detection=0.99, \
prob_sanctioning=0.99, prob_random_punishment=0.01):
"""Scenarios return plan libraries and sets of norms"""
goal = Goal('a', 'd')
planlib = set([
Action(['a', 'b']),
Action(['b', 'e']),
Action(['b', 'c']),
Action(['b', 'd']),
Action(['a', 'f']),
Action(['a', 'c', 'e']),
Action(['e', 'd'])
])
norms = set([('a', 'never', 'e')])
scenario = Scenario(norms, planlib, goal, prob_non_compliance, prob_viol_detection, \
prob_sanctioning, prob_random_punishment)
return scenario
def setUp(self):
super(BayesianNormDetectorTest, self).setUp()
self.normdetector = bayesian_norm_detector(self.planlib,
goal=Goal('a', 'd'))
prob_compliance_and_norm_abiding_path = 0
else:
prob_path_via_norm_abiding_plans = len(
norm_abiding_plans_containing_path) / len(
norm_abiding_plans)
prob_compliance_and_norm_abiding_path = (
1 - self.prob_non_compliance
) * prob_path_via_norm_abiding_plans
return self.prob_non_compliance * prob_path_if_no_norm + prob_compliance_and_norm_abiding_path
else:
raise ValueError(
"Invalid hypothesis passed to Likelihood function: %s" %
hypothesis)
goal = Goal('a', 'd')
actions = set([
Action(['a', 'b']),
Action(['b', 'e']),
Action(['b', 'c']),
Action(['b', 'd']),
Action(['a', 'f']),
Action(['a', 'c', 'e']),
Action(['e', 'd'])
])
observation = ['a', 'c', 'e', 'd']
nodes = {node for action in actions for path in action.path for node in path}
poss_norms = [
norm for node in nodes for norm in ("forbidden", node), ("obliged", node)
]
from data_mining_norm_detector import data_mining_norm_detector
from planlib import Action, Goal
goal = Goal('a', 'd')
planlib = set([
Action(['a', 'b']),
Action(['b', 'e']),
Action(['b', 'c']),
Action(['b', 'd']),
Action(['a', 'f']),
Action(['a', 'c', 'e']),
Action(['e', 'd'])
])
observation1 = ['a', 'c', '!', 'e', 'd']
observation2 = ['a', 'b', 'd', '!']
observation3 = ['a', 'b', 'e', 'd', '!']
dmnt = data_mining_norm_detector(planlib, Goal('a', 'd'))
dmnt.reinitialise()
dmnt.update_with_observations(observation1)
dmnt.update_with_observations(observation2)
dmnt.update_with_observations(observation3)
numTopNorms = 3
norms = dmnt.get_inferred_norms(numTopNorms)
print "Inferred norms: ", norms
n = len(norms)
if n != numTopNorms:
print "Asked for", numTopNorms, "norms"
print n, "norms returned due to ties or zero probabilities"