OVERVIEW
 Pac man Background
o Literature Survey – References
o Motivation
 Problem Statement
 What is the goal? / What are we trying to achieve?
o Pathfinding algorithms
o Adversarial search techniques using local search (minimax)
o PEAS for our implementation
 Design and Implementation
o Design
 Refer to Berkeley – basic framework – GUI – Code
integration
 Data Structures used : Priority Queue
o Implementation
 Minimax implementation details(Chetan and James) –
REPORT
 Non -Adversarial Pathfinding - DFS, BFS, Astar, UCS,
Greedy
 Adversarial Pathfinding – Minimax +
random/directional/hill climbing
 Reflex Pacman – Hillclimbing/Random
ghosts/Directional(manhattan distance)
 REINFORCEMENT – OPTIONAL
 Evaluation
o Path finding algorithm for pacman under
 Various algorithms
o Minimax pacman – maze solving
 Hill climbing ghosts
 Directional ghosts
 Random ghosts
 Challenges
o TSP – finding optimal heuristic to complete the mazes
o Pacman compete against ghosts
 Inference
 Future Work