Classifying Agent Behaviour through Relational Sequential Patterns

Abstract. In Multi-Agent System, observing other agents and modelling their behaviour represents an essential task: agents must be able to quickly adapt to the environment and infer knowledge from other agents’ deportment. The observed data from this kind of environments are inherently sequential. We present a relational model to characterise adversary teams based on its behaviour using a set of relational sequences in order to classify them. We propose to use a relational learning algorithm to mine meaningful features as frequent patterns among the relational sequences and use these features to construct a feature vector for each sequence and then to compute a similarity value between sequences. The sequence extraction and classification are implemented in the domain of simulated robotic soccer, and experimental results are presented. Key words: Sequence Data Mining, Sequence Classification, Relational Sequence Similarity, Adversary Classification, Group Behaviour

• Type: Conference
• Year: 2010
• Where: KESAMSTA
• Authors: Grazia Bombini, Nicola Di Mauro, Stefano Ferilli, Floriana Esposito
• Keywords: Intelligent Agents, KESAMSTA 2010, Relational Learning Algorithm, Relational Sequence, Relational Sequence Similarity