Agent Clustering Based on Semantic Negotiation

Forming groups of agents is an important task in many agent-based applications, for example when determining a coalition of buyers in an e-commerce community or organizing different Web services in a Web services' composition. A key issue in this context is that of generating groups of agents such that the communication among agents of the same group is not subjected to comprehension problems. To this purpose, several approaches have been proposed in the past in order to form groups of agents based on some similarity measures among agents. Such similarity measures are mainly based on lexical and/or structural similarities among agent ontologies. However, the necessity of taking into account a semantic component of the similarity value arises, for example by considering the context in which a term is used in an agent ontology. Therefore we propose a clustering technique based on the HISENE semantic negotiation protocol, using a similarity value that has lexical, structural and semantic components. Moreover, we introduce a suitable multiagent architecture that allows computing agent similarities by means of an efficient distributed approach.