class WumpusAgent:
def __init__(self,x,y):
# load the KB rules
self.kb = KB('fluent_agent')
# set the size of the environment
self.kb.tell("x_size(%d)" % x)
self.kb.tell("y_size(%d)" % y)
# initialize the agent
self.kb.ask("agt_initialize")
def action(self, percept):
# Note: this version is slightly different from the one in the
# book. It is left up to the KB code to keep track of the
# situation and update its knowledge with the new percept.
# make a prolog term from the percept list
# strings become symbols (no quotes around them).
percept_str = "[" + ",".join(percept)+"]"
#print percept
# form a simple
query = "agt_act(%s,A)" % percept_str
# perform the query and extract the answer
#print query
#safes = self.kb.ask("setof(Loc1, (location(Loc1), safe_unvisited(locDir(Loc1, _))), SafeCells)")
#if safes is not None:
# print safes['SafeCells']
ans = self.kb.ask(query)
#print ans
return ans['A']
def __init__(self,x,y):
# load the KB rules
self.kb = KB('fluent_agent')
# set the size of the environment
self.kb.tell("x_size(%d)" % x)
self.kb.tell("y_size(%d)" % y)
# initialize the agent
self.kb.ask("agt_initialize")