Collective perception in massive, open, and heterogeneous multi-agent environment

Abstract

The web of interconnected intelligent software agents as well as intelligent hardware agents will be seamlessly embedded in everywhere of our lives and constantly sensing and reacting to the environment. The dynamic and heterogeneous interactions among these agents will provide great opportunities for agent-based services. One of the challenging issues in this agent-based service environment is the task of collective perception: how to make sense of complex sensed data at the conceptual level by a group of collaborative agents. This paper proposes a strategy for collective perception when the agents involved may not share the same knowledge representation or ontology. To avoid the syntax, semantics, and ontological complexities in communicating and understanding among agents, the synthesizing agent collects only the analyzed and categorized results from other agents in the form of a natural number or a vector of natural numbers. It then perform collective perception on top of these categorized results. An eigenspace method is proposed to model and perceive events. Experimental results are presented to show the effectiveness of our mechanism.