Agents teaching agents to share meaning
Andrew B. Williams, Zijian Ren · 2001
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.