Welcome to InfOCF-Web
InfOCF-Web [Beierle et al. 2024 (SUM)] is a tool for reasoning with conditional belief bases.
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 belief base consists of a set of conditionals. Different semantical models have been proposed for conditional belief 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.
Inductive Reasoning
The most important reasoning problem for conditional belief bases is to determine which consequences a belief base entails. Some of the approaches to specifying the entailments of a conditional belief base are system P [Lehmann and Magidor, 1992] taking all models into account, system Z [Pearl, 1990] taking the unique minimal OCF into account, lexicographic inference, which computes an ordering of worlds based on the number of falsified conditionals [Lehmann, 1995], inference with respect to a single c-representation [Kern-Isberner, 2001 (LNCS), Kern-Isberner, 2004 (AMAI)], 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)], or system W that extends both system Z and c-inference [Komo and Beierle 2020 (KI), Komo and Beierle 2020 (AMAI)].
InfOCF-Web provides an easy-to-use online tool for computing and comparing a variety of different inference relations induced by a belief base. An overview of background information and the inductive inference operators realized by InfOCF-Web is given in [Beierle et al. 2024 (SUM)]. For further details, we refer to the papers cited on this page. InfOCF-Web is based on the library InfOCF [Beierle et al. 2025 (JELIA)]. InfOCF and InfOCF-Web use the .cl syntax specified in the online repository of conditional logic resources for knowledge representation and reasoning CLKR [Beierle et al. 2024 (KI)].
Access
The current version of InfOCF-Web is InfOCF-Web 2.2, scaling up reasoning from conditional belief bases significantly, and allowing for signatures of 100+ propositional variables and belief bases containing 100+ conditionals [Beierle et al., 2022 (FLAIRS), von Berg et al., 2022 (ECSQARU), von Berg et al., 2024 (FoIKS), Beierle et al., 2024 (FLAIRS), Haldimann et al., 2025 (ECSQARU)]. InfOCF-Web 2.2. supports dealing with both strongly and weakly consistent conditional belief bases [Golszmidt and Pearl, 1996 (Art.Int.), Haldimann et al. 2023 (FLAIRS), Haldimann et al. 2025 (KER), Beierle et al. 2026 (FLAIRS)].
A previous version of InfOCF-Web [Kutsch, Beierle 2021 (IJCAI)] 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)].
It was later extended by a first implementation of system W [Beierle et al., 2022 (KI)].
How to cite
If you wish to cite InfOCF-Web, please use (Beierle et al. 2024 (SUM)) [Beierle et al. 2024 (SUM)]. Download bibtex entry.
Feedback
We welcome your feedback regarding this tool. Please send us your comments and hints by email: christoph.beierle@fernuni-hagen.de