Rule-based and Ontology-based Policies: Toward a Hybrid Approach to Control Agents in Pervasive Environments

Policies are being increasingly used for controlling the behavior of complex multi-agent systems. The use of policies allows administrators to regulate agent behavior without changing source code or requiring the consent or cooperation of the agents being governed. However, policy-based control can sometimes encounter difficulties when applied to agents that act in pervasive environments characterized by frequent and unpredictable changes. In such cases, we cannot always specify policies a priori to handle any operative run time situation, but instead require continuous adjustments to allow agents to behave in a contextually appropriate manner. To address these issues, some policy approaches for governing agents in pervasive environments specify policies in a way that is both context-based and semantically-rich. Two approaches have been used in recent research: an ontology-based approach that relies heavily on the expressive features of Description Logic (DL) languages, and a rule-based approach that encodes policies as Logic Programming (LP) rules. The aim of this paper is to analyze the emerging directions for the specification of semantically-rich context-based policies, highlighting their advantages and drawbacks. Based on our analysis we describe a hybrid approach that exploits the expressive capabilities of both DL and LP approaches.