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Large-scale Parallel Stratified Defeasible Reasoning

Tachmazidis, Ilias, Antoniou, Grigoris, Flouris, Giorgos, Kotoulas, Spyros and McCluskey, T.L. (2012) Large-scale Parallel Stratified Defeasible Reasoning. In: Frontiers in Artificial Intelligence and Applications: Proceedings of ECAI 2012. IOS Press, pp. 738-743. ISBN 978-1-61499-097-0

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      Abstract

      We are recently experiencing an unprecedented explosion of available data from the Web, sensors readings, scientific databases, government authorities and more. Such datasets could benefit from the introduction of rule sets encoding commonly accepted rules or facts, application- or domain-specific rules, commonsense knowledge etc. This raises the question of whether, how, and to what extent knowledge representation methods are capable of handling huge amounts of data for these applications. In this paper, we consider inconsistency-tolerant reasoning in the form of defeasible logic, and analyze how parallelization, using the MapReduce framework, can be used to reason with defeasible rules over huge datasets. We extend previous work by dealing with predicates of arbitrary arity, under the assumption of stratification. Moving from unary to multi-arity predicates is a decisive step towards practical applications, e.g. reasoning with linked open (RDF) data. Our experimental results demonstrate that defeasible reasoning with millions of data is performant, and has the potential to scale to billions of facts.

      Item Type: Book Chapter
      Additional Information: Paper presented at 20th European Conference on Artificial Intelligence, 27–31 August 2012, Montpellier, France
      Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
      Schools: School of Computing and Engineering
      School of Computing and Engineering > Informatics Research Group > Knowledge Engineering and Intelligent Interfaces
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      Depositing User: Thomas Mccluskey
      Date Deposited: 05 Nov 2012 15:45
      Last Modified: 05 Nov 2012 15:47
      URI: http://eprints.hud.ac.uk/id/eprint/15918

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