1,042 research outputs found
Reason Maintenance - State of the Art
This paper describes state of the art in reason maintenance with a focus on its future usage in the KiWi project. To give a bigger picture of the field, it also mentions closely related issues such as non-monotonic logic and paraconsistency. The paper is organized as follows: first, two motivating scenarios referring to semantic wikis are presented which are then used to introduce the different reason maintenance techniques
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Value-based argumentation frameworks as neural-symbolic learning systems
While neural networks have been successfully used in a number of machine learning applications, logical languages have been the standard for the representation of argumentative reasoning. In this paper, we establish a relationship between neural networks and argumentation networks, combining reasoning and learning in the same argumentation framework. We do so by presenting a new neural argumentation algorithm, responsible for translating argumentation networks into standard neural networks. We then show a correspondence between the two networks. The algorithm works not only for acyclic argumentation networks, but also for circular networks, and it enables the accrual of arguments through learning as well as the parallel computation of arguments
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
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