The Evolution of Strategies for Multiagent Environments

Abstract

SAMUEL is an experimental learning system that uses genetic algorithms and other learning methods to evolve reactive decision rules from simulations of multiagent environments. The basic approach is to explore a range of behavior within a simulation model, using feedback to adapt its decision strategies over time. One of the main themes in this research is that the learning system should be able to take advantage of existing knowledge where available. This has led to the adoption of rule representations that ease the expression of existing knowledge. A second theme is that adaptation can be driven by competition among knowledge structures. Competition is applied at two levels in SAMUEL. Within a strategy composed of decision rules, rules compete with one another to influence the behavior of the system. At a higher level of granularity, entire strategies compete with one another, driven by a genetic algorithm. This article focuses on recent elaborations of the agent model of SAMUEL that are specifically designed to respond to multiple external agents. Experimental results are presented that illustrate the behavior of SAMUEL on two multiagent predator-prey tasks.