def preprocess(self):
# Project the initial state
new_init = PDKB(self.init.depth, self.init.agents, self.init.props)
rmls = self.init.rmls
for ag in self.agent_projection:
rmls = project(rmls, ag)
new_init.rmls = rmls
self.init = new_init
self.orig_cond_count = 0
self.comp_cond_count = 0
# Expand and project the action's effects with derived conditional effects
for act in self.domain.actions:
self.orig_cond_count += sum([
len(act.effs[i][0]) + len(act.effs[i][1])
for i in range(len(act.effs))
])
act.expand()
self.comp_cond_count += sum([
len(act.effs[i][0]) + len(act.effs[i][1])
for i in range(len(act.effs))
])
for ag in self.agent_projection:
act.project_effects(ag)
if self.agent_projection:
# We project the precondition separately since we are in the
# perspective of the final agent
act.project_pre(self.agent_projection[-1])
def test1():
print "\n----\n"
l1 = Literal('p')
l2 = Belief(2, Belief(1, l1))
l3 = Belief(2, neg(Belief(1, neg(l1))))
l4 = neg(l3)
l5 = neg(Belief(1, neg(Belief(2, l1))))
l6 = neg(Belief(1, neg(Belief(2, Possible(3, Belief(4, l1))))))
print "KD closure operation:"
print "%s -> %s" % (str(l2), str(map(str, kd_closure(l2))))
print "%s -> %s" % (str(l3), str(map(str, kd_closure(l3))))
print "\n----\n"
kb = PDKB(2, [1,2], map(Literal, ['p', 'q']))
kb.add_rml(l2)
print "Initial KB:"
print kb
print "\nClosing..."
kb.logically_close()
print kb
print "\n----\n"
kb.add_rml(l4)
print "Initial Closed KB:"
kb.logically_close()
print kb
print "\nConsistent: %s" % str(kb.is_consistent())
print "\n----\n"
kb = PDKB(2, [1,2], map(Literal, ['p', 'q']))
kb.add_rml(Belief(1, Literal('p')))
kb.add_rml(Belief(2, Literal('q')))
kb.add_rml(Belief(1, Belief(2, Literal('q'))))
kb.add_rml(neg(Belief(1, Literal('q'))))
print "Initial KB:"
print kb
print "\n...projecting on agent 1\n"
print project(kb.rmls, 1)
print "\n----\n"
def preprocess(self):
# Project the initial state
new_init = PDKB(self.init.depth, self.init.agents, self.init.props)
rmls = self.init.rmls
for ag in self.agent_projection:
rmls = project(rmls, ag)
new_init.rmls = rmls
self.init = new_init
# Expand and project the action's effects with derived conditional effects
for act in self.domain.actions:
act.expand()
for ag in self.agent_projection:
act.project_effects(ag)
if self.agent_projection:
# We project the precondition separately since we are in the
# perspective of the final agent
act.project_pre(self.agent_projection[-1])