RELATIONAL SEQUENCE BASED CLASSIFICATION IN MULTI-AGENT SYSTEMS
Grazia Bombini, Nicola Di Mauro, Stefano Ferilli, Floriana Esposito · 2010
In multiagent adversarial environment, the adversary consists of a team of opponents that may interfere with the achievement of goals. In this domain agents must be able to quickly adapt to the environment and infer knowledge from other agents ’ deportment to identify the future behaviors of opponents. We present a relational model to characterize adversary teams based on its behavior. A team’s deportment is represent by a set of relational sequences of basic actions extracted from their observed behaviors. Based on this, we present a similarity measure to classify the teams ’ behavior. The sequence extraction and classification are implemented in the domain of simulated robotic soccer, and experimental results are presented. 1