def next_victim(self, world):
"""
Generate an expectation about what room the player will enter next
"""
player = world.agents[self.parser.player_name()]
action = world.getAction(player.name, unique=True)
if action['verb'] == 'triage_Green':
# Triaging green as we speak
return Distribution({'Green': 1})
elif action['verb'] == 'triage_Gold':
# Triaging yellow as we speak
return Distribution({'Yellow': 1})
# Not so obvious who will be next
agent = world.agents['ATOMIC']
beliefs = agent.getBelief()
if len(beliefs) == 1:
agent_model, agent_beliefs = next(iter(beliefs.items()))
else:
raise NotImplementedError(
'Unable to generate predictions unless agent has unique model')
location = world.getState(player.name, 'loc', unique=True)
prediction = None
for player_model, player_model_prob in world.getModel(
player.name, agent_beliefs).items():
player_beliefs = player.models[player_model]['beliefs']
fov = world.getState(player.name,
'vicInFOV',
player_beliefs,
unique=True)
if fov in {'Yellow', 'Green'}:
# The next victim found is the one the player is looking at now
next_seen = Distribution({fov: 1})
else:
# The next victim found is one in the player's current location
next_seen = {
'Yellow':
world.getState(WORLD, 'ctr_{}_Gold'.format(location),
player_beliefs).expectation(),
'Green':
world.getState(WORLD, 'ctr_{}_Green'.format(location),
player_beliefs).expectation()
}
if sum(next_seen.values()) == 0:
# No victim in the current room
next_seen = {'Yellow': 1, 'Green': 1}
next_seen = Distribution(next_seen)
next_seen.normalize()
if prediction is None:
prediction = next_seen.scale_prob(player_model_prob)
else:
prediction = prediction.__class__({
color: prob + next_seen[color] * player_model_prob
for color, prob in prediction.items()
})
return prediction
def collapseProbabilistic(self):
"""
Utility method that combines any consecutive probabilistic branches at this node into a single distribution
"""
if self.isProbabilistic():
collapse = False
distribution = Distribution(self.children)
for child in self.children.domain():
if child.isProbabilistic():
# Probabilistic branch to merge
collapse = True
child.collapseProbabilistic()
del distribution[child]
for grandchild in child.children.domain():
try:
distribution[grandchild] += self.children[child]*child.children[grandchild]
except KeyError:
distribution[grandchild] = self.children[child]*child.children[grandchild]
if collapse:
assert sum(distribution.values()) == 1.
self.makeProbabilistic(distribution)