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CS188_P4_Ghostbusters Implemented Pacman agents that "bust ghosts"using Hidden Markov Models and Particle Filtering. Project 4 for CS188 - "Introduction to Artificial Intelligence" at UC Berkeley during Spring 2020.
In the CS 188 version of Ghostbusters, the goal is to hunt down scared but invisible ghosts. Pacman, ever resourceful, is equipped with sonar (ears) that provides noisy readings of the Manhattan distance to each ghost.
- Introduction
- Ghostbusters and BNS
- Approximate Inference
Pacman spends his life running from ghosts, but things were not always so. Legend has it that many yearsago, Pacman's great grandfather Grandpac learned to hunt ghosts for sport. However, he was blinded by hispower and could only track ghosts by their banging and clanging. In this project, you will design Pacman agents that use sensors to locate an...
In the cs188 version of Ghostbusters, the goal is to hunt down scared but invisible ghosts. Pacman, ever resourceful, is equipped with sonar (ears) that provides noisy readings of the Manhattan distance to each ghost. The game ends when Pacman has eaten all the ghosts. To start, try playing a game yourself using the keyboard. The blocks of color in...
Approximate inference is very trendy among ghost hunters this season. Next, you will implement a particle filtering algorithm for tracking a single ghost. Question 4 (5 points) Implement all necessary methods for the ParticleFilter class in inference.py. A correct implementation should also handle two special cases. (1) When all your particles rece...
- (3 points): Exact Inference Observation. In this question, you will update the observe method in ExactInference class of inference.py to correctly update the agent's belief distribution over ghost positions given an observation from Pacman's sensors.
- (4 points): Exact Inference with Time Elapse. In the previous question you implemented belief updates for Pacman based on his observations. Fortunately, Pacman's observations are not his only source of knowledge about where a ghost may be.
- (3 points): Exact Inference Full Test. Now that Pacman knows how to use both his prior knowledge and his observations when figuring out where a ghost is, he is ready to hunt down ghosts on his own.
- (3 points): Approximate Inference Observation. Approximate inference is very trendy among ghost hunters this season. Next, you will implement a particle filtering algorithm for tracking a single ghost.
AI Pacman Bayesian inference demonstration
- 2 min
- 903
- antonis chronakis
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The game ends when Pacman has eaten all the ghosts. To start, try playing a game yourself using the keyboard. <pre> python busters.py</pre> <p>The blocks of color indicate where the each ghost could possibly be, given the noisy distance readings provided to Pacman. The noisy distances at the bottom of the display are always non-negative, and ...
