Analysing the behaviour of robot teams through relational sequential pattern mining
Grazia Bombini, Raquel Ros, Stefano Ferilli, Ramon Lopez de Mantaras

TL;DR
This paper presents a relational sequential pattern mining method to analyze and compare team behaviors in multi-agent systems, demonstrated through RoboCup competitions, enabling systematic verification of cooperation.
Contribution
It introduces an unsupervised relational learning approach to model and recognize multi-agent team behaviors from observations using symbolic sequences.
Findings
Relational sequences effectively characterize team behaviors.
The method distinguishes different team strategies in RoboCup.
Pattern mining reveals key behavioral differences.
Abstract
This report outlines the use of a relational representation in a Multi-Agent domain to model the behaviour of the whole system. A desired property in this systems is the ability of the team members to work together to achieve a common goal in a cooperative manner. The aim is to define a systematic method to verify the effective collaboration among the members of a team and comparing the different multi-agent behaviours. Using external observations of a Multi-Agent System to analyse, model, recognize agent behaviour could be very useful to direct team actions. In particular, this report focuses on the challenge of autonomous unsupervised sequential learning of the team's behaviour from observations. Our approach allows to learn a symbolic sequence (a relational representation) to translate raw multi-agent, multi-variate observations of a dynamic, complex environment, into a set of…
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Taxonomy
TopicsLogic, Reasoning, and Knowledge · Semantic Web and Ontologies · Data Mining Algorithms and Applications
