Feature-based declarative opponent-modelling

Abstract

In the growing area of multi-agent-systems (MAS) also the diversity of the types of agents within these systems grows. Agent designers can no longer hard-code all possible interaction situations into their software, because there are many types of agents to be encountered. Thus, agents have to adapt their behavior online depending on the encountered agents. This paper proposes that agent behavior can be classified by distinct and stable tactical moves, called features, on different levels of granularity. The classification is used to select appropriate counter-strategies. While the overall framework is aimed to be applicable in a wide range of domains, the feature-representation in the case-base and the counter-strategies is done in a domain-specific language. In the RoboCup domain the standard coach-language is used. The approach has been successfully evaluated in a number of experiments.