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KESAMSTA
2010
Springer

Classifying Agent Behaviour through Relational Sequential Patterns

10 years 2 months ago
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
Grazia Bombini, Nicola Di Mauro, Stefano Ferilli,
Added 19 Jul 2010
Updated 19 Jul 2010
Type Conference
Year 2010
Where KESAMSTA
Authors Grazia Bombini, Nicola Di Mauro, Stefano Ferilli, Floriana Esposito
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