Representation results for defeasible logic

Antoniou, Grigoris; Billington, David P.; Governatori, Guido; Maher, Michael J.

doi:10.1145/371316.371517

Cited by 275 publications

(300 citation statements)

References 15 publications

(27 reference statements)

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“…We prove 1, and 2 is a consequence of 1 and the principle of strong negation [4]. For M = K see [5], since the conditions for knowledge are those for derivability in DL. At the same time, we have that ∆ + K ⊆ ∂ + K .…”

Section: Defeasible Logic Of Intention and Bdimentioning

confidence: 99%

“…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%

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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: Defeasible Logic Of Intention and Bdimentioning

confidence: 99%

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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“…Our formalism, called Process Compliance Language (PCL), is a combination of Defeasible Logic (DL) [7] and a deontic logic of violations [6]. PCL significantly extends the logic of [3] with types of obligations discussed in Section 2 and preserves the linear complexity of DL.…”

Section: Process Compliance Language (Pcl)mentioning

confidence: 99%

Norm Compliance in Business Process Modeling

Governatori

Rotolo

2010

Lecture Notes in Computer Science

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Abstract. We investigate the concept of norm compliance in business process modeling. In particular we propose an extension of Formal Contract Logic (FCL), a combination of defeasible logic and a logic of violation, with a richer deontic language capable of capture many different facets of normative requirements. The resulting logic, called Process Compliance Logic (PCL), is able to capture both semantic compliance and structural compliance. This paper focuses on structural compliance, that is we show how PCL can capture obligations concerning the structure of a business process.

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“…However, we hope to give enough information to make the discussion intelligible. We refer the reader to [24,10,4] for more thorough treatments. As usual with non-monotonic reasoning, we have to specify 1) how to represent a knowledge base and 2) the inference mechanism.…”

Section: Defeasible Logicmentioning

confidence: 99%

“…For this reason an attempt has been made, by using a language which has proven successful in the field of ontologies and combining its principles with properties of a more flexible logic, to solve in part the motivating problem driving this work. The proposed method for solving this is to integrate two established forms of logic, Description logic [8] and Defeasible logic [4,10,24]. Description logics though imparting strong and conclusive reasoning mechanisms, lack the flexibility of Defeasible logics non-monotonic reasoning mechanisms, which add flexibility to knowledge bases that have partial knowledge.…”

Section: Introductionmentioning

confidence: 99%