Reflecting the Perspectives of Multiple Agents in Distributed Reasoning for Context-Aware Service

Abstract

Effective manipulation of context is very important in providing the context-aware services. In recent years, a variety of context models have been proposed to p roperly handle the key aspects of the context, while focusing on scenario-based acquisition, management, and representation of context. However, they are difficult to be employed for the agent-based system requiring distributed reasoning. In this paper we propose a context modeling approach for distributed reasoning and merge operator reflecting the perspective of constituent agents for rational reasoning. In addition, an agent-based context-aware system is developed implementing the proposed scheme. Performance evaluation by computer simulation on a use case of smart classroom shows that the proposed approach enables the agents to rationally reason and thereby provide intelligent context -aware services to the users.

II-Learn—A Novel Metric for Measuring the Intelligence Increase and Evolution of Artificial Learning Systems

“…Intelligent systems have been successfully applied for the solution of a variety of difficult practical problems, such as medical diagnosis [1][2][3][4][5][6], intrusion detection [7], network traffic anomalies detection [8], multisensor battlefield reconnaissance simulation [9], local semantic indexing for resource discovery on overlay networks [10], distributed reasoning for context-aware service [11] and real-time water demand management [12]. Given a problem, its solving difficulty can be considered from different viewpoints, for example, from the human and the computational viewpoint.…”