Document Type

Conference Proceeding

Publisher

IEEE

Faculty

Computing, Health and Science

School

Computer and Information Science

RAS ID

5415

Comments

This article was originally published as: Wittkamp, M., Barone, L., & Hingston, P. F. (2008). Using NEAT for Continuous Adaptation and Teamwork Formation in Pacman. Proceedings of IEEE Symposium on Computational Intelligence and Games. (pp. 234-242). Australia, Perth. IEEE. Original article available here

Abstract

Despite games often being used as a testbed for new computational intelligence techniques, the majority of artificial intelligence in commercial games is scripted. This means that the computer agents are non-adaptive and often inherently exploitable because of it. In this paper, we describe a learning system designed for team strategy development in a real time multi-agent domain. We test our system in the game of Pacman, evolving adaptive strategies for the ghosts in simulated real time against a competent Pacman player. Our agents (the ghosts) are controlled by neural networks, whose weights and structure are incrementally evolved via an implementation of the NEAT (Neuro-Evolution of Augmenting Topologies) algorithm. We demonstrate the design and successful implementation of this system by evolving a number of interesting and complex team strategies that outperform the ghosts' strategies of the original arcade version of the game.

DOI

10.1109/CIG.2008.5035645

 
COinS
 

Link to publisher version (DOI)

10.1109/CIG.2008.5035645