An abstract representation model for evolutionary analysis of multi-agent interactions

Intelligent agents are able to assist human in managing highly dynamic and complex systems in various knowledge intensive domains. The communication between different agents interacting in an integrated multi-agents system can be managed through a set of steering rules, which together form interaction protocols. To support the negotiation, communication and interaction between different intelligent agents, using an appropriate knowledge representation formalism is crucial. This paper introduces the potential of category theory as a formal representation vehicle to facilitate evolutionary analysis of agent interaction and negotiation for managing evolving ontologies in the domain of biomedicine. Utilizing categories supports agents' communication, negotiation, state transitions, compositions and transformations in different levels of abstractions.