Agents teaching agents to share meaning

Abstract

The promise of intelligent agents acting on behalf of users' personalized knowledge sharing needs may be hampered by the insistence that these agents begin with a predefined, common ontology instead of personalized, diverse ontologies. Only until recently have researchers diverged from the last decades common “ontology paradigm” to a paradigm involving agents that can share knowledge using diverse ontologies. This paper describes how we address this agent knowledge sharing problem of how agents deal with diverse ontologies by introducing a methodology and algorithms for multi- agent knowledge sharing and learning. We demonstrate how this approach will enable multi- agent systems to assist groups of people in locating, translating, and sharing knowledge using our Distributed Ontology Gathering Group Integration Environment (DOGGIE) and describe our proof- of- concept experiments. DOGGIE synthesizes agent communication, machine learning, and reasoning for information sharing in the Web domain.