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# CS188 Introduction to Artificial Intelligence - Project 3: Logic and Classical Planning

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# Sometimes Pacman is just really lost and in the dark at the same time. In SLAM (Simultaneous Localization and Mapping), Pacman knows his initial coordinates, but does not know where the walls are. In SLAM, Pacman may inadvertently take illegal actions (for example, going North when there is a wall blocking that action), which will add to the uncertainty of Pacman's location over time. Additionally, in our setup of SLAM, Pacman no longer has a 4 bit sensor that tells us whether there is a wall in the four directions, but only has a 3-bit sensor that reveals the number of walls he is adjacent to. (This is sort of like wifi signal-strength bars; 000 = not adjacent to any wall; 100 = adjacent to exactly 1 wall; 110 = adjacent to exactly 2 walls; 111 = adjacent to exactly 3 walls. These 3 bits are represented by a list of 3 booleans.) Thus, instead of using sensorAxioms and four_bit_percept_rules, you will use SLAMSensorAxioms and num_adj_walls_percept_rules. You will code up the sentences that help Pacman determine (1) his possible locations at each timestep, and (2) the location of the walls, by implementing: slam(problem, agent): Given an instance of logicPlan.SLAMProblem and logicAgents.SLAMLogicAgent, returns a tuple of two items:list of known_maps per timestep (of the same format as in question 6 (mapping))list of possible pacman locations per timestep (of the same format as in question 5 (localization))To pass the autograder, please implement the function according to our pseudocode: Get initial location (pac_x_0, pac_y_0) of Pacman, and add this to KB.for t in range(agent.num_timesteps):Add pacphysics, action, sensor, and percept information to KB.Find provable wall locations with updated KBFind possible pacman locations with updated KB.Call agent.moveToNextState(...) on the current agent action at timestep t.Add to KB: SLAMSuccessorAxioms(...).return known_map_by_timestep, possible_locs_by_timestepTo test and debug your code run: python autograder.py -q q8

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