A Logic for Context-Aware Non-monotonic Reasoning Agents

Rakib, Abdur; Haque, Hafiz Mahfooz Ul

doi:10.1007/978-3-319-13647-9_41

Cited by 20 publications

(54 citation statements)

References 20 publications

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“…We extend the logical framework presented in [5] by incorporating the notion of multi-context systems where rules are derived from heterogeneous semantic knowledge sources. The system consists of n Ag (≥ 1) individual agents Ag = {1, 2, ...., n Ag }.…”

Section: Multi-agent Model Over Heterogeneous Knowledge Sourcesmentioning

confidence: 99%

“…The formal context modeling and reasoning about context is one of the fundamental research areas in context-aware computing. In the literature, various context modeling and reasoning approaches have been proposed, including ontology and rule-based approach [3][4][5]. In our previous work [4,5], we have developed formal logical frameworks and shown how context-aware systems can be modeled as multi-agent reasoning agents.…”

Section: Introductionmentioning

confidence: 99%

“…In the literature, various context modeling and reasoning approaches have been proposed, including ontology and rule-based approach [3][4][5]. In our previous work [4,5], we have developed formal logical frameworks and shown how context-aware systems can be modeled as multi-agent reasoning agents. A formal logical model allows us to capture a system's behavior in a systematic and precise way.…”

Section: Introductionmentioning

confidence: 99%

“…The first level is based on multi-context system to model heterogeneous systems similar to contextual ontologies studied by [9]. For the second level, we follow the approach proposed in our previous work [4,5], where a context is formally defined as a (subject, predicate, object) triple that states a fact about the subject where -the subject is an entity in the environment, the object is a value or another entity, and the predicate is a relationship between the subject and object.…”

Section: Introductionmentioning

confidence: 99%

“…The main contributions of this paper are: first, we extend our previous work [5] by introducing a different modeling approach to deal with distributed context handling considering more than one domain. This approach is novel in a sense that context-aware agents use contextual information which are extracted from different knowledge sources.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Modeling and Reasoning about Preference-Based Context-Aware Agents over Heterogeneous Knowledge Sources

et al. 2017

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This paper presents a conceptual framework and multi-agent model for context-aware decision support in dynamic smart environments based on heterogeneous knowledge sources. A Protégé plug-in for rules extraction from distributed ontologies has been developed, which allows us to model context-aware agents using the notion of multi-context systems. Extracted rules can be annotated to match the users' needs and to develop a preference model to support their preferences so as to provide a user with a more personalized services. The use of the proposed framework

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Section: Multi-agent Model Over Heterogeneous Knowledge Sourcesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling and Reasoning about Preference-Based Context-Aware Agents over Heterogeneous Knowledge Sources

et al. 2017

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Contextual defeasible reasoning framework for heterogeneous knowledge sources

Haque

Akhtar

Uddin

2021

Concurrency and Computation

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Recent years have witnessed the rapid advances of smart computing paradigms in a ubiquitous environment. These paradigms make human life much easier, comfortable, secure and hassle free. In a smart computing environment, it is a fact that human users interact with the systems dynamically with or without human intervention using different modalities. The core emphasize is given on the intelligent systems that run in a highly decentralized environment with different communication mechanism. Literature highlighted numerous formalisms to bridge the communication modalities for different knowledge sources. Among others, Multi-context System (MCS) has been advocated as one of the most suitable formalism to interlink different contexts (domains) dynamically in the distributed environment. However, interaction of these knowledge sources sometime may produce inconsistent and conflicting results. In this work, we presents a contextual defeasible reasoning based multi-agent formalism to handle the inconsistency issues. This framework relies on the semantic knowledge sources which allow us to model context-aware non-monotonic reasoning agents to infer the desired goals using the extracted rules from the ontologies and handles inconsistencies using conflicting contextual information. We illustrate the validity and correctness of the proposed formalism using a simple case study of a smart healthcare system with the prototypal implementation of the system.

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Contextual Defeasible Reasoning Framework for Heterogeneous Systems

Akhtar

Haque

2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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A Logic for Context-Aware Non-monotonic Reasoning Agents

Cited by 20 publications

References 20 publications

Modeling and Reasoning about Preference-Based Context-Aware Agents over Heterogeneous Knowledge Sources

Modeling and Reasoning about Preference-Based Context-Aware Agents over Heterogeneous Knowledge Sources

Contextual defeasible reasoning framework for heterogeneous knowledge sources

Contextual Defeasible Reasoning Framework for Heterogeneous Systems

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