Abstract
We propose a logical framework for modelling and verifying context-aware multi-agent systems. We extend CTL? with belief and communication modalities, and the resulting logic ??OCRS allows us to describe a set of rule-based reasoning agents with bound on time, memory and communication. The set of rules which are used to model a desired systems is derived from OWL 2 RL ontologies. We provide an axiomatization of the logic and prove it is sound and complete. We show how Maude rewriting system can be used to encode and verify interesting properties of ??OCRS models using existing model checking techniques.
Highlights
The vision of pervasive computing technology intends to provide invisible computing environments so that a user can utilize services at any time and everywhere [28]
We propose a logical framework based on the earlier work of Alechina and colleagues [4, 5, 3], and the resulting LOCRS logic allows us to describe a set of ontology-driven rule-based reasoning agents with bounds on time, memory, and communication
We prove that M is in M(nM, nC) by showing that Manager Agent (7), Palliative Care Unit (PCU) Coordinator it satisfies all properties listed in Definition 4
Summary
The vision of pervasive computing technology intends to provide invisible computing environments so that a user can utilize services at any time and everywhere [28]. Other issues include space requirements for reasoning and the number of messages that are exchanged between tiny resource-bounded devices in order to achieve their goals This is because memory space of such a device is relatively small and its life time is inversely proportional to the number of messages it exchanges. Various logical frameworks have been developed for modelling and verification of multi-agent systems (a brief state-of-the-art survey can be found in [22, 21]) Such frameworks may not be very suitable to model context-aware applications. We propose a logical framework based on the earlier work of Alechina and colleagues [4, 5, 3], and the resulting LOCRS logic allows us to describe a set of ontology-driven rule-based reasoning agents with bounds on time, memory, and communication.
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