Pacman spends his life running from ghosts, but things were not always so. Legend has it that many years ago, Pacman's great grandfather Grandpac learned to hunt ghosts for sport. However, he was blinded by his power and could only track ghosts by their banging and clanging. In this project, I will design Pacman agents that use sensors to locate and eat invisible ghosts. I will advance from locating single, stationary ghosts to hunting packs of multiple moving ghosts with ruthless efficiency. In the Pacman 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.
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 always within 7 of the true distance. The probability of a distance reading decreases exponentially with its difference from the true distance. My primary task in this project is to implement inference to track the ghosts. Naturally, we want a better estimate of the ghost's position. Fortunately, Bayes' Nets provide powerful tools for making the most of the information we have. Throughout the rest of this project, I will implement algorithms for performing both exact and approximate inference using Bayes' Nets.
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