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This paper outlines the use of a relational representation in a Multi-Agent domain to model the behaviour of the whole system. The aim of this work is to define a general systematic method to verify the effective collaboration among the members of a team and to compare the different multi-agent behaviours, using external observations of a Multi-Agent System. Observing and analysing the behavior of a such system is a difficult task. Our approach allows to learn sequential behaviours from raw multi-agent observations of a dynamic, complex environment, represented by a set of sequences expressed in first-order logic. In order to discover the underlying knowledge to characterise team behaviours, we propose to use a relational learning algorithm to mine meaningful frequent patterns among the relational sequences. We compared the performance of two soccer teams in a simulated environment, each based on very different behavioural approaches: While one uses a more deliberative strategy, the other one uses a pure reactive one.

Analysing the Behaviour of Robot Teams through Relational Sequential Pattern Mining