Argumentation Semantics for Defeasible Logics

Defeasible logic is a simple but efficient rule-based non-monotonic logic. It has powerful implementations and shows promise to be applied in the areas of legal reasoning and the modelling of business rules. So far defeasible logic has been defined only proof-theoretically. Argumentation-based semantics have become popular in the area of logic programming. In this paper we give an argumentation-based semantics for defeasible logic. Recently it has been shown that a family of approaches can be built around defeasible logic, in which different intuitions can be followed. In this paper we present an argumentation-based semantics for an ambiguity propagating logic, too. Further defeasible logics can be characterised in a similar way

An Argumentation-Theoretic Characterization of Defeasible Logic

Defeasible logic is an efficient non-monotonic logic that is defined only proof-theoretically. It has potential application in some legal domains. We present here an argumentation semantics for defeasible logic that will be useful in these applications. Our development differs at several points from existing argumentation frameworks since there are several features of defeasible logic that have not been addressed in the literature

Embedding Defeasible Logic into Logic Programming

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.Comment: To appear in Theory and Practice of Logic Programmin

Argumentation Semantics for Defeasible Logics

Defeasible reasoning is a simple but efficient rule-based approach to nonmonotonic reasoning. It has powerful implementations and shows promise to be applied in the areas of legal reasoning and the modeling of business rules. This paper establishes significant links between defeasible reasoning and argumentation. In particular, Dung-like argumentation semantics is provided for two key defeasible logics, of which one is ambiguity propagating and the other ambiguity blocking. There are several reasons for the significance of this work: (a) establishing links between formal systems leads to a better understanding and cross-fertilization, in particular our work sheds light on the argumentation-theoretic features of defeasible logic; (b) we provide the first ambiguity blocking Dung-like argumentation system; (c) defeasible reasoning may provide an efficient implementation platform for systems of argumentation; and (d) argumentation-based semantics support a deeper understanding of defeasible reasoning, especially in the context of the intended applications

A Family of Defeasible Reasoning Logics and its Implementation

Defeasible reasoning is a direction in nonmonotonic reasoning that is based on the use of rules that may be defeated by other rules. It is a simple, but often more efficient approach than other nonmonotonic reasoning systems. This paper presents a family of defeasible reasoning formalisms built around Nute's defeasible logic. We describe the motivations of these formalisms and derive some basic properties and interrelationships. We also describe a query answering system that supports these formalisms and is available on the World Wide Web

Current and Future Challenges in Knowledge Representation and Reasoning

Knowledge Representation and Reasoning is a central, longstanding, and active area of Artificial Intelligence. Over the years it has evolved significantly; more recently it has been challenged and complemented by research in areas such as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl Perspectives workshop was held on Knowledge Representation and Reasoning. The goal of the workshop was to describe the state of the art in the field, including its relation with other areas, its shortcomings and strengths, together with recommendations for future progress. We developed this manifesto based on the presentations, panels, working groups, and discussions that took place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge Representation: its origins, goals, milestones, and current foci; its relation to other disciplines, especially to Artificial Intelligence; and on its challenges, along with key priorities for the next decade