Relational Sequence Clustering for Aggregating Similar Agents

Many clustering methods are based on flat descriptions, while data regarding real-world domains include heterogeneous objects related to each other in multiple ways. For instance, in the field of Multi-Agent System, multiple agents interact with the environment and with other agents. In this case, in order to act effectively an agent should be able to recognise the behaviours adopted by other agents. Actions taken by an agent are sequential, and thus its behaviour can be expressed as a sequence of actions. Inferring knowledge about competing and/or companion agents by observing their actions is very beneficial to construct a behavioural model of the agent population. In this paper we propose a clustering method for relational sequences able to aggregate companion agent behaviours. The algorithm has been tested on a real world dataset proving its validity. Key words: Sequence Clustering, Relational Sequence Similarity

• Type: Conference
• Year: 2009
• Where: ISMIS
• Authors: Grazia Bombini, Nicola Di Mauro, Stefano Ferilli, Floriana Esposito
• Keywords: Artificial Intelligence, Clustering Method, Companion Agent, ISMIS 2009, Many Clustering Methods