Efficient defeasible reasoning systems

Maher, Michael J.; Rock, Andrew; Antoniou, Grigoris; Billington, David P.; Miller, Tristan

doi:10.1109/tai.2000.889898

Cited by 48 publications

(85 citation statements)

References 29 publications

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“…Defeasible logic, as developed by Nute [51,50] with a particular concern about computational efficiency and developed over the years by [9,5,4] is our choice. The reason being ease of implementation [46], flexibility [4] (it has a constructively defined and easy to use proof theory), modularity [5] and it is efficient: it is possible to compute the complete set of consequences of a given theory in linear time [44]. We do not address any semantic issues in this paper but the argumentation semantics as given in [32] could be straightforwardly extended to the present case.…”

Section: Overview Of Defeasible Logicmentioning

confidence: 99%

A defeasible logic for modelling policy-based intentions and motivational attitudes

Governatori

Padmanabhan

Rotolo

et al. 2009

Logic Journal of IGPL

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In this paper we show how defeasible logic could formally account for the non-monotonic properties involved in motivational attitudes like intention and obligation. Usually, normal modal operators are used to represent such attitudes wherein classical logical consequence and the rule of necessitation comes into play, i.e., A/ 2A, that is from A derive 2A. This means that such formalisms are affected by the Logical Omniscience problem. We show that policy-based intentions exhibit nonmonotonic behaviour which could be captured through a non-monotonic system like defeasible logic. To this end we outline a defeasible logic of intention that specifies how modalities can be introduced and manipulated in a non-monotonic setting without giving rise to the problem of logical omniscience. In a similar way we show how to add deontic modalities defeasibly and how to integrate them with other motivational attitudes like beliefs and goals. Finally we show that the basic aspect of the BOID architecture is captured by this extended framework.

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Section: Overview Of Defeasible Logicmentioning

confidence: 99%

A defeasible logic for modelling policy-based intentions and motivational attitudes

Governatori

Padmanabhan

Rotolo

et al. 2009

Logic Journal of IGPL

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“…Deimos [17] is a flexible, query processing system based on Haskell. It implements several variants, but not conflicting literals nor negation as failure in the object language.…”

Section: Related Workmentioning

confidence: 99%

“…Delores [17] is another implementation, which computes all conclusions from a defeasible theory. It is very efficient, exhibiting linear computational complexity.…”

Section: Related Workmentioning

confidence: 99%

A Graphical Rule Authoring Tool for Defeasible Reasoning in the Semantic Web

Bassiliades

Kontopoulos

Antoniou

et al. 2005

Advances in Informatics

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Abstract. Defeasible reasoning is a rule-based approach for efficient reasoning with incomplete and inconsistent information. Such reasoning is useful for many applications in the Semantic Web, such as policies and business rules, agent brokering and negotiation, ontology and knowledge merging, etc. However, the syntax of defeasible logic may appear too complex for many users. In this paper we present a graphical authoring tool for defeasible logic rules that acts as a shell for the DR-DEVICE defeasible reasoning system over RDF metadata. The tool helps users to develop a rule base using the OO-RuleML syntax of DR-DEVICE rules, by constraining the allowed vocabulary through analysis of the input RDF namespaces, so that the user does not have to type-in class and property names. Rule visualization follows the tree model of RuleML. The DR-DEVICE reasoning system is implemented on top of the CLIPS production rule system and builds upon an earlier deductive rule system over RDF metadata that also supports derived attribute and aggregate attribute rules.

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“…[22]) and ease of implementation (nowadays several implementations exist [11,23] and some of them can deal with theories consisting of over 100,000 propositional rules [23]). In [3] it was shown that Defeasible logic is flexible enough to deal with several intuitions of non-monotonic reasoning, and it has been applied to legal reasoning [2,16], automated negotiation [14,12], contracts [27], business rules [20], and multi-agent systems [18,17,15,16].…”

Section: Defeasible Logicmentioning

confidence: 99%