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.
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Relational Sequence based Classification in Multi-agent Systems
Semantic Scholar · Computer Science · 2010
Abstract
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.