On the analysis of regulations using defeasible rules

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

doi:10.1109/hicss.1999.772631

Cited by 57 publications

(43 citation statements)

References 8 publications

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“…Defeasible logic is closely related to several non-monotonic logics (Antoniou et al 1999). In particular, the "directly skeptical" semantics of non-monotonic inheritance networks (Horty et al 1987) can be considered an instance of inference in DL once an appropriate superiority relation, derived from the topology of the network, is fixed (Billington et al 1990).…”

Section: I) and T > Smentioning

confidence: 99%

“…Its use in various application domains has been advocated, including the modelling of regulations and business rules (Morgensten 1998;Antoniou et al 1999), modelling of contracts (Grosof et al 1999;Grosof 2004;Governatori 2005), legal reasoning (Prakken 1997;Governatori et al 2005), agent negotiations , modelling of agents and agent societies (Governatori and Rotolo 2004;Governatori and Padmanabhan 2003;, and applications to the Semantic Web (Bassiliades et al 2004;. In fact, defeasible reasoning (in the form of courteous logic programs (Grosof 1997;Grosof et al 1999)) provides a foundation for IBM's Business Rules Markup Language and for current W3C activities on rules (Grosof and Poon 2003;Grosof et al 2002).…”

Section: Introductionmentioning

confidence: 99%

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Embedding defeasible logic into logic programming

Antoniou

Billington

Governatori

et al. 2006

Theory and Practice of Logic Programming

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Defeasible reasoning is a simple but efficient approach to nonmonotonic reasoning that has recently attracted considerable interest and that has found various applications. Defeasible logic and its variants are an important family of defeasible reasoning methods. So far no relationship has been established between defeasible logic and mainstream nonmonotonic reasoning approaches.In this paper we establish close links to known semantics of logic programs. In particular, we give a translation of a defeasible theory D into a meta-program P (D). We show that under a condition of decisiveness, the defeasible consequences of D correspond exactly to the sceptical conclusions of P (D) under the stable model semantics. Without decisiveness, the result holds only in one direction (all defeasible consequences of D are included in all stable models of P (D)). If we wish a complete embedding for the general case, we need to use the Kunen semantics of P (D), instead.

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Section: I) and T > Smentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Embedding defeasible logic into logic programming

Antoniou

Billington

Governatori

et al. 2006

Theory and Practice of Logic Programming

View full text Add to dashboard Cite

show abstract

“…In particular, "if the spell of no crisis is long enough, the regulation level may drop to zero, despite the fact that the socially optimal regulation level remains positive" [Aizenman 2009]. This implies an optimal number of rules that avoids these two extremes [Barro 1986]. This paper seeks to develop models that qualitatively illustrate this behavior and can be used as a starting point for a more detailed, quantitative approach for determining the optimal number of rules in a particular environment.…”

Section: Introductionmentioning

confidence: 99%

Percolation Model of insider threats to assess the optimum number of rules

2015

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Abstract-Rules, regulations, and policies are the basis of civilized society and are used to coordinate the activities of individuals who have a variety of goals and purposes. History has taught that over-regulation (too many rules) makes it difficult to compete and under-regulation (too few rules) can lead to crisis. This implies an optimal number of rules that avoids these two extremes. Rules create boundaries that define the latitude an individual has to perform their activities. This paper creates a Toy Model of a work environment and examines it with respect to the latitude provided to a normal individual and the latitude provided to an insider threat. Simulations with the Toy Model illustrate four regimes with respect to an insider threat: underregulated, possibly optimal, tipping-point, and over-regulated. These regimes depend up the number of rules (N) and the minimum latitude (L min ) required by a normal individual to carry out their activities. The Toy Model is then mapped onto the standard 1D Percolation Model from theoretical physics and the same behavior is observed. This allows the Toy Model to be generalized to a wide array of more complex models that have been well studied by the theoretical physics community and also show the same behavior. Finally, by estimating N and L min it should be possible to determine the regime of any particular environment.

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“…Defeasible reasoning can represent facts, rules as well as priorities and conflicts among rules. Such conflicts arise, among others, from rules with exceptions, which are a natural representation for policies and business rules [2]. And priority information is often available to resolve conflicts among rules.…”

Section: Introductionmentioning

confidence: 99%

Visualizing Defeasible Logic Rules for the Semantic Web

Kontopoulos

Bassiliades

Antoniou

2006

The Semantic Web – ASWC 2006

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Abstract. Defeasible reasoning is a rule-based approach for efficient reasoning with incomplete and conflicting information. Such reasoning is useful in many Semantic Web applications, like policies, business rules, brokering, bargaining and agent negotiations. Nevertheless, defeasible logic is based on solid mathematical formulations and is, thus, not fully comprehensible by end users, who often need graphical trace and explanation mechanisms for the derived conclusions. Directed graphs can assist in confronting this drawback. They are a powerful and flexible tool of information visualization, offering a convenient and comprehensible way of representing relationships between entities. Their applicability, however, is balanced by the fact that it is difficult to associate data of a variety of types with the nodes and the arcs in the graph. In this paper we try to utilize digraphs in the graphical representation of defeasible rules, by exploiting the expressiveness and comprehensibility they offer, but also trying to leverage their major disadvantage, by defining two distinct node types, for rules and atomic formulas, and four distinct connection types for each rule type in defeasible logic and for superiority relationships. The paper also briefly presents a tool that implements this representation methodology.

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On the analysis of regulations using defeasible rules

Cited by 57 publications

References 8 publications

Embedding defeasible logic into logic programming

Embedding defeasible logic into logic programming

Percolation Model of insider threats to assess the optimum number of rules

Visualizing Defeasible Logic Rules for the Semantic Web

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