AI in Game Playing: Sokoban Solver
Anand Venkatesan, Atishay Jain, Rakesh Grewal
TL;DR
This paper develops and compares AI algorithms for solving Sokoban, a classical Japanese game, to evaluate their effectiveness using standard performance metrics.
Contribution
It introduces a framework for applying and benchmarking multiple AI algorithms and heuristics specifically for Sokoban puzzle solving.
Findings
Different algorithms show varying efficiency in solving Sokoban puzzles.
Heuristics significantly improve the solving speed of AI agents.
Performance metrics help identify the most effective strategies for Sokoban AI.
Abstract
Artificial Intelligence is becoming instrumental in a variety of applications. Games serve as a good breeding ground for trying and testing these algorithms in a sandbox with simpler constraints in comparison to real life. In this project, we aim to develop an AI agent that can solve the classical Japanese game of Sokoban using various algorithms and heuristics and compare their performances through standard metrics.
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Taxonomy
TopicsArtificial Intelligence in Games · Educational Games and Gamification