Pacman berkeley solution. The purpose of this project was to learn found...

Pacman berkeley solution. The purpose of this project was to learn foundational AI concepts, such as informed state-space search, probabilistic inference, and reinforcement learning. The multiagent problem requires modeling an adversarial and a stochastic search agent using minimax algorithm with alpha-beta pruning and expectimax algorithms, as well as designing evaluation functions. Back in 2011, I took the original Introduction to Artificial Intelligence online course taught by Peter Norving and Sebastian Thrun. However, he was blinded by his power and could only track ghosts by their banging and clanging. Contribute to PointerFLY/Pacman-AI development by creating an account on GitHub. I thoroughly enjoyed all the AI theory we learnt but I desperately needed to apply those to solve problems. This project is devoted to implementing adversarial agents so would fit into the online class right about now. Solutions of 1 and 2 Pacman projects of Berkeley AI course - PetropoulakisPanagiotis/pacman-projects About the projects The Pac-Man projects were developed for UC Berkeley’s introductory artificial intelligence course, CS 188. Credits The Pacman AI projects were developed at UC Berkeley. This is a popular project used at multiple different universities, but it originated with this course. jpct piurrvv wfxfq fhbk puu iqhxi zfnyj lmhbhz biot apjl