Reflective multi-agent model using semantic similarity measure and negotiation protocol for solving heterogeneity

Abstract

Semantic similarity measures play important role in communication in an open and heterogeneous multi-agent system (MAS). A survey on similarity measures between concepts is afforded in this paper. We present these techniques, provide evaluations of their result performances, and discuss their shortcomings. We propose a measure by combining a psychological knowledge of the relevance, the resemblance and the non-symmetry of similarity. The proposed measure leads us to suggest a novel reflective agent model allowing agents to autonomously communicate between each other through semantic heterogeneity. The agent can enrich its own ontology by using semantic negotiation approach in several steps. We develop firstly, a model using an alignment ontology framework. Then, we improve a similarity measure to select the most similar pairs. Then, we suggest a protocol for supporting semantic negotiation. At the end, we present our experiments on many benchmark datasets proving that our results are more reasonable, we provide evaluations of some result performances of the existing semantic similarity metrics, and discuss their advantages and drawbacks.