InfOCF-Web

Default rules of the form "If A, then usually B" are powerful constructs for knowledge representation. Such rules can be formalized as conditionals, denoted by (B|A). A conditional knowledge base consists of a set of conditionals. Different semantical models have been proposed for conditional knowledge bases, including quantitative, semi-quantitative, and qualitative approaches. E.g., an ordinal conditional function (OCF) [Spohn, 1988], ordering possible worlds according to their degree of surprise, accepts the conditional (B|A) if it considers a world where A holds, but B does not hold, to be strictly more surprising than a world where both A and B are true.

The most important reasoning problems for conditional knowledge bases are to determine whether a knowledge base is consistent and to determine what a knowledge base entails. Some of the approaches to specifying the entailments of a conditional knowledge base are system P [Lehmann and Magidor, 1992] taking all models into account, system Z [Pearl, 1990] taking a unique minimal OCF into account, inference with respect to a single c-representation [Kern-Isberner, 2001 (LNCS), Kern-Isberner, 2004 (AMAI)], or c-inference relations realizing various modes of inference and taking different classes of c-representations into account [Beierle et al., 2016 (FoIKS), Beierle et al., 2016 (ECAI), Beierle et al., 2018 (AMAI), Beierle et al., 2021 (AIJ)].

InfOCF-Web is a tool for reasoning with conditional knowledge bases. The objective of InfOCF-Web is to provide an easy-to-use online tool for computing and comparing a variety of different inference relations induced by a knowledge base. For background information and for the formal definition of the inference methods provided by InfOCF-Web, we refer to the papers cited above and to the list of publications given here.

InfOCF-Web is implemented using the Java library InfOCF-Lib [Kutsch, 2019], providing sophisticated representation and reasoning methods for conditional logic and ranking functions [Kutsch, 2020], and a Prolog backend, providing a constraint satisfaction problem solver [Beierle et al., 2017 (KI)].

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References

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