In this case, I implemented the ExpectimaxAgent, which is useful for modeling probabilistic behavior of agents who may make suboptimal choices. As anyone who has ever won tic-tac-toe can tell you, this is not always the case. Minimax and alpha-beta are great, but they both assume that you are playing against an adversary who makes optimal decisions. This is not a problem: as it is correct behaviour, it will pass the tests. The correct implementation of alpha-beta pruning will lead to Pacman losing some of the tests. Python pacman.py -p AlphaBetaAgent -a depth=3 -l smallClassic Ideally, depth 3 on smallClassic should run in just a few seconds per move or faster. Blinky is the red ghost, and is the one who will be most aggressive about chasing Pac-Man around. You should see a speed-up (perhaps depth 3 alpha-beta will run as fast as depth 2 minimax). Make a new agent that uses alpha-beta pruning to more efficiently explore the minimax tree, in AlphaBetaAgent. Python pacman.py -p MinimaxAgent -l minimaxClassic -a depth=4 Alpha-Beta Pruning This will show what your algorithm does on a number of small trees, as well as a pacman game. If you call it any more or less than necessary, the autograder will complain. As a result, the autograder will be very picky about how many times you call GameState.generateSuccessor. This is the only way reliable way to detect some very subtle bugs in implementations of minimax. Grading: We will be checking your code to determine whether it explores the correct number of game states. Important: A single search ply is considered to be one Pacman move and all the ghosts' responses, so depth 2 search will involve Pacman and each ghost moving two times. MinimaxAgent extends MultiAgentSearchAgent, which gives access to pth and self.evaluationFunction. Score the leaves of your minimax tree with the supplied self.evaluationFunction. The code should also expand the game tree to an arbitrary depth. In particular, the minimax tree has multiple min layers (one for each ghost) for every max layer. Minimax agent works with any number of ghosts. Pac-Man Pac-Man World 3 Ghosts, Pac Man, blue, text, rectangle png 650圆50px 7.67KB Yellow Desktop Circle, pacman, color, sphere, area png 600x595px 10.98KB Pacman pixel illustration against blue background, Pac-Man Minecraft Pixel art Xbox 360, Pac Man, text, smiley, video Game png 1200x1200px 7. Wrote an adversarial search agent in the provided MinimaxAgent class stub in multiAgents.py. Python autograder.py -q q1 -no-graphics Minimax Turn off graphics with -q to run lots of games quickly. You can also play multiple games in a row with -n. If the randomness is preventing you from telling whether your agent is improving, you can use -f to run with a fixed random seed (same random choices every game). Python pacman.py -frameTime 0 -p ReflexAgent -k 2ĭefault ghosts are random you can also play for fun with slightly smarter directional ghosts using -g DirectionalGhost. Python pacman.py -frameTime 0 -p ReflexAgent -k 1 Python pacman.py -p ReflexAgent -l testClassic Implemented reflex agent to consider both food locations and ghosts locations to perform well Proceedings of Level Up: Digital Games Research Conference, Utrecht, Netherlands.Design Agents for the classic version of Pac-Man including ghosts. Who are the Pac-Man ghosts There are four ghosts in the original Pac-Man (which we rated as top of the best Pac-Man games ): a red ghost, a pink ghost, a cyan ghost and an orange ghost. "Expressive AI: Games and Artificial Intelligence" (PDF). They then base the direction they will go in accordance to their programming. They make their decisions only right before being at junctions like corners or intersections. monster topic, The arcade cabinet refered to the enemies as "monsters", but the when the Atari 2600 home version was released with pale, flickering enemies, the manual dubbed them "ghosts", to add to the confusion, the TV series refers to them as "ghost monsters" (I personally prefer ghosts.) The ghosts actually have specific instructions when it comes to chasing (Ms.) Pac-man. Orange: Pokey " Clyde" In Japan, his character is described as otoboke, meaning “pretending ignorance”, and his nickname is “Guzuta”, meaning “one who lags behind”. (Shy and tries to stay away from pac-man, unless approached too much, after which it will chase Pac-Man too.) (Fast but random movement.)Ĭyan: Bashful " Inky" In Japan, he is portrayed as kimagure, meaning “a fickle, moody, or uneven temper”. Pink: Speedy " Pinky" In Japan, he is characterized as machibuse, meaning “to perform an ambush”. (Chases Pac-Man all the time, Also speeds up after you've eaten a certain amount of dots, which gets less for each level.) Red: Shadow " Blinky" In Japan, his character is represented by the word oikake, which means “to run down or pursue”. The Character (Personality), nicknames and behavior for the enemies are:
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