Document Type
Conference Proceeding
Publisher
IEEE
Faculty
Faculty of Computing, Health and Science
School
School of Computer and Security Science / Artificial Intelligence and Optimisation Research Group
RAS ID
14792
Abstract
Learning team-based strategies in real-time is a difficult task, much more so in the presence of noise. In our previous work in the Prey and Predators domain we introduced an algorithm capable of evolving cooperative team strategies in real-time using fitness evaluations against a perfect opponent model. This paper continues our work within the same domain, training a team of predators to capture a prey. We investigate the effect of varying degrees of opponent model noise in our learning system. In the presence of and in the effort to mitigate the effects of such noise we present modifications to our baseline system in the forms of Rescaled Mutation, Conservative Replacement and a combination of the two techniques. The results of the modifications are extremely promising. The combined approach in particular demonstrates a vast improvement and decreased variance in the performance of our team of predators in the presence of opponent model noise. Additionally, the noise-mitigating strategies employed do not adversely affect the performance of the real-time team learning system in the absence of noise.
DOI
10.1109/CIG.2012.6374134
Access Rights
free_to_read
Comments
This is an Author's Accepted Manuscript of: Wittkamp, M., Barone, L., Hingston, P. F., & While, L. (2012). Noise Tolerance for Real-time Evolutionary Learning of Cooperative Predator-Prey Strategies. Proceedings of IEEE Conference on Computational Intelligence and Games (CIG). (pp. 25-32). Granada, Spain. IEEE. Available here
© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.