Implemented various AI algorithms in Pac-Man projects developed by UC Berkeley. Implemented informed/blind state-space search using search algorithms like BFS, DFS, UCS and A* algorithm with heuristic calculation. Designed an algorithm for reflex agent, minimax and alpha-beta pruning. Reinforcement Learning using MDP (Value Iteration and Policy Evaluation) - Implemented Markov Decision process using Value iteration in the Pacman World. Designed perceptron classifier for data classification. Developed applications using python. http://ai.berkeley.edu/project_overview.html

Berkeley AI Pacman Project for developing search agents to play Pacman

Solutions to Pacman AI Multi-Agent Search problems

🕹️👻👾👻 In this thrilling AI adventure, we embark on a multi-stage quest to transform Pacman into an intelligent game-playing agent. Across three engaging projects, we explore various facets of artificial intelligence, from basic search algorithms to adversarial competition and reinforcement learning.

• Implemented Finding a Fixed Food Dot strategy using DFS & BFS and also find lowest cost path using Uniform Cost Search (UCS) Algorithm. Also build A* Algorithm which used heuristic to finds the optimal solution which is slightly faster than UCS. • Finding all the corners of the maze using admissible, consistent and non-trivial Heuristic was another part of the project. Last part of the project was implementation of algorithm for eating all the Pacman food in as few steps as possible and Suboptimal food Search.

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Berkeley AI Pacman single agent search program implemented using BFS-DFS-A*

Implementation of an AI that plays the Pacman game using: Search agents, Minimax agents and a Bayes filter.

Pacman agent based on Monte Carlo tree search. Code for homework of CSE 415 (AI).

This project is based on the UC Berkeley CS188 Pacman AI framework, implementing and testing algorithms and multi-agent search strategies for Pacman.

Developed several different AI algorithms for a pacman bot to demonstrate these algorithms in a set state. These AI algorithms took the forms of search, multi-agents, and reinforcement learning.

This project implements various AI agents to play the classic Pacman game. The agents use different search algorithms, including Reflex Agent, Minimax, Alpha-Beta Pruning, and Expectimax. Additionally, custom evaluation functions are designed to improve the performance of these agents.

This task focuses on implementing Pacman search algorithms—depth-first, breadth-first, uniform-cost, and A*. It introduces a maze challenge, requiring coding of search agents. The document provides clear instructions and insights, offering a comprehensive introduction to AI in gaming.

This is the Pacman AI agent implemented as a search problem with DFS, UCS, BFS, A* with different heuristic functions. This project was a part of Artificial Intelligence Nanodegree @Udacity.

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Implementation of classic AI algorithms in the Pacman framework. Includes search (DFS, BFS, UCS, A*), heuristic and adversarial agents, and puzzle solvers. Built in Python with reproducible demos and clear commands.

Built autonomous agents that efficiently make decisions in fully informed, partially observable, and adversarial settings.

AI agents for Pacman using classical search algorithms (DFS, BFS, UCS, A*) and heuristic-based decision making.

Artificial Intelligence university project focused on pathfinding and game agents.

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Developing AI search agents to win Pacman.

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Implementing Pacman as an AI search agent

pa3 - pacman multi-agent search for ai class

AI agents for Pacman using reinforcement learning and search algorithms.

An AI agent for Pacman using different search algorithms

Pacman AI Search Agent project for CS 188 @ UC Berkeley.

Multi-agent search version of berkeley-s AI pacman project.

Implementation of Pacman Artificial Intelligent agents. Uses AI concepts: Search, Adverserial Search, Reinforcement Learning, Probabilistic Inference, and neural network models to build rational pacman agents. to

AI agent that plays pacman. Implemented three different agents that use A* search, iterative deepening search and depth limited search.