CILIOS: Connectionist Inductive Learning and Inter-Ontology Similarities for Recommending Information Agents

For a software information agent, operating on behalf of a human owner and belonging to a community of agents, thechoice of communicating or not with another agent becomes a decision to take, since communication generally implies acost. Since these agents often operate as recommender systems, on the basis of dynamic recognition of their human owners’behaviour and by generally using hybrid machine learning techniques, three main necessities arise in their design, namely(i) providing the agent with an internal representation of both interests and behaviour of its owner, usually called ontology;(ii) detecting inter-ontology properties that can help an agent to choose the most promising agents to be contacted forknowledge-sharing purposes; (iii) semi-automatically constructing the agent ontology, by simply observing the behaviourof the user supported by the agent, leaving to the user only the task of defining concepts and categories of interest. Wepresent a complete MAS architecture, called connectionist learning and inter-ontology similarities (CILIOS), forsupporting agent mutual monitoring, trying to cover all the issues above. CILIOS exploits an ontology model able torepresent concepts, concept collections, functions and causal implications among events in a multi-agent environment;moreover, it uses a mechanism capable of inducing logical rules representing agent behaviour in the ontology by means ofa connectionist ontology representation, based on neural-symbolic networks, i.e., networks whose input and output nodesare associated with logic variables.