Large-scale Parallel Stratified Defeasible Reasoning

Publication Date: 
Wednesday, 29 August, 2012
Published in: 
20th European Conference on Artificial Intelligence (ECAI 2012)
Ilias Tachmazidis, Grigoris Antoniou, Giorgos Flouris, Spyros Kotoulas and Lee McCluskey

This paper will be published in Proceedings of the 20th European Conference on Artificial Intelligence (ECAI 2012) held in Montpellier, France on August 27th - 30th, 2012 (to appear). ECAI 2012, the 20th conference in this series, will be jointly organized by the European Coordination Committee for Artificial Intelligence (ECCAI), the French Association for Artificial Intelligence (AFIA) and Montpellier Laboratory for Informatics, Robotics and Microelectronics (LIRMM).


We are recently experiencing an unprecedented explosion of available data coming 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.