Control Systems and Computers, N4, 2024, Article 3
Control Systems and Computers, 2024, Issue 4 (308), pp.
UDC 004.94
O.A. NOVIKOV, PhD student, Institute of Computer Science and Artificial Intelligence Department of Mathematical Modeling and Data Analysis, Karazin Kharkiv National University, Svobody, Sq 4, Kharkiv, Ukraine, 61022, ORCID: https://orcid.org/0009-0004-5914-7098, artem.slick@gmail.com
V.V. YANOVSKY, Doctor of Physical and Mathematical Sciences, Professor of Department of Artificial Intelligence Systems, “Institute for Single Crystals” of National Academy of Sciences, Nauky ave. 60, Kharkiv, 61001, Ukraine, ORCID: https://orcid.org/0000-0003-0461-749X, yanovsky@isc.kharkov.ua
ANALYSIS OF SEARCH AND MULTI-AGENT ALGORITHMS
IN THE PAC-MAN GAME
This paper examines the performance of search and multi-agent algorithms within the context of the Pac-Man game. The game is used as a platform to simulate autonomous system management tasks, where an agent must complete missions in a two-dimensional space while avoiding dynamic obstacles. Classical search algorithms such as A* and BFS, along with multi-agent approaches like Alpha-Beta, Expectimax, and Monte Carlo Tree Search (MCTS), are analyzed in terms of their effectiveness under different maze complexities and game conditions. The study explores how maze size, ghost behaviors, and environmental dynamics influence the performance of each algorithm, particularly in terms of execution time, score, and win percentage.
Keywords: search algorithms, multi-agent algorithms, Pac-Man, path optimization, Alpha-Beta, Expectimax, MCTS.
