*Course: Artifcial intellgance*

Problem :Your Agent Bot should Grab the yellow piece in the Wumpus Grid World of the MP2Game Class object and Climb from the initial cell more than 300 times out of 1000 tries using the Theorem-Proving approach.

And With only Python's basic filtering capabilities, your rational agent should also locate the Wumpus whenever it is possible using the Model-Checking and shoot it.

(Please compelet the code )

x=cntsx y=cntsy \# update your agent's knowledge base \# c..................... here if (sensors [] aw= "Stench") \& Wumpus_alive: # \%..................... here else: \# \&............................ here if sensors [1] == "Breeze"; # .......................... here else: . and here if sensors [2] =a "Glitter": cnts.action( "Grab") if sensors [4] == "Seream": print( "Wumpus Killed") \# cnts.go_to ( LineSight_x, LineSightyy) \# cnts.shoot_at( Wumpuslocation_ x, Wumpuslocation_y) \# continue \# Find the next best move frontier = ents, frontier () frontier:sort (key=lambda k : abs(k[0]-x)+abs(k[1]-y) ) destination =[1,0] for d in frontier: .... if the nulL hypothesis is unsatisfiable (50LVE destination =d break \# use cnts. action( "Turnkight"), cnts. action( "Turnleft"), and response = cnts. actior response = cnts.action( destination) \# just keep this line for simplification. if not response: break time. sleep (.2)