A multi-agent system for enabling collaborative situation awareness via position-based stigmergy and neuro-fuzzy learning

Abstract

Situation awareness is a computing paradigm which allows applications to sense parameters in the environment, comprehend their meaning and project their status in the next future. In collaborative situation awareness, a challenging area in the field of Ambient Intelligence applications, situation patterns emerge from users' collective behavior. In this paper we introduce a multi-agent system that exploits positioning information coming from mobile devices to detect the occurrence of user's situations related to social events. In the functional view of the system, the first level of information processing is managed by marking agents which leave marks in the environment in correspondence to the users' positions. The accumulation of marks enables a stigmergic cooperation mechanism, generating short-term memory structures in the local environment. Information provided by such structures is granulated by event agents which associate a certainty degree with each event. Finally, an inference level, managed by situation agents, deduces user situations from the underlying events by exploiting fuzzy rules whose parameters are generated automatically by a neuro-fuzzy approach. Fuzziness allows the system to cope with the uncertainty of the events. In the architectural view of the system, we adopt semantic web standards to guarantee structural interoperability in an open application environment. The system has been tested on different real-world scenarios to show the effectiveness of the proposed approach.