Contextual defeasible reasoning framework for heterogeneous knowledge sources

被引:1
|
作者
ul Haque, Hafiz Mahfooz [1 ]
Akhtar, Salwa Muhammad [2 ]
Uddin, Ijaz [3 ]
机构
[1] Univ Lahore, Dept Software Engn, Lahore, Pakistan
[2] Univ Lahore, Dept Comp Sci, Lahore, Pakistan
[3] City Univ Sci & Informat Technol, Dept Comp Sci, Peshawar, Pakistan
来源
关键词
Context-awareness; Contextual Defeasible Reasoning; Multi-agent System; Multi-context System; Semantic Knowledge Sources; ONTOLOGIES;
D O I
10.1002/cpe.6446
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
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.
引用
收藏
页数:15
相关论文
共 50 条
  • [31] Defeasible reasoning with legal conditionals
    Gazzo Castaneda, Lupita Estefania
    Knauff, Markus
    MEMORY & COGNITION, 2016, 44 (03) : 499 - 517
  • [32] Heuristics, justification, and defeasible reasoning
    Colburn, TR
    MINDS AND MACHINES, 1995, 5 (04) : 467 - 487
  • [33] Demonstrating a Benchmark for Defeasible Reasoning
    Hecham, Abdelraouf
    Croitoru, Madalina
    Bisquert, Pierre
    COMPUTATIONAL MODELS OF ARGUMENT (COMMA 2018), 2018, 305 : 461 - 462
  • [34] Defeasible Reasoning and Degrees of Justification
    Pollock, John L.
    ARGUMENT & COMPUTATION, 2010, 1 (01) : 7 - 22
  • [35] Computational Dialogic Defeasible Reasoning
    Robert L. Causey
    Argumentation, 2003, 17 (4) : 421 - 450
  • [36] Modelling and Reasoning About Context-Aware Agents over Heterogeneous Knowledge Sources
    Ul Haque, Hafiz Mahfooz
    Rakib, Abdur
    Uddin, Ijaz
    CONTEXT-AWARE SYSTEMS AND APPLICATIONS (ICCASA 2016), 2017, 193 : 1 - 11
  • [37] Defeasible reasoning and informal fallacies
    Douglas Walton
    Synthese, 2011, 179 : 377 - 407
  • [38] Formalizing perspectival defeasible reasoning
    Kim, HG
    THIRTIETH HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES, VOL 5: ADVANCED TECHNOLOGY, 1997, : 347 - 353
  • [39] Negativity bias in defeasible reasoning
    Gazzo Castaneda, Lupita Estefania
    Richter, Bruno
    Knauff, Markus
    THINKING & REASONING, 2016, 22 (02) : 209 - 220
  • [40] Semantics for a theory of defeasible reasoning
    Quoc Bao Vo
    Norman Y. Foo
    Joe Thurbon
    Annals of Mathematics and Artificial Intelligence, 2005, 44 : 87 - 119