KLM-Style Defeasible Reasoning for Datalog - Paterson-Jones, Guy (Department of Computer Science, 2023)
In many problem domains, particularly those related to mathematics and philosophy, classical logic has enjoyed great success as a model of valid reasoning and discourse. For real-world reasoning tasks, however, an agent typically only has partial knowledge of its domain, and at most a statistical understanding of relationships between properties. In this context, classical inference is considered overly restrictive, and many systems for non-monotonic reasoning have been proposed in the literature to deal with these tasks. A notable example is the Klm framework, which describes an agent's defeasible knowledge qualitatively in terms of conditionals of the form “if A, then typically B”. The goal of this research project is to investigate Klm-style semantics for defeasible reasoning over Datalog knowledge bases. Datalog is a declarative logic programming language, designed for querying large deductive databases. Syntactically, it can be viewed as a computationally feasible fragment of firstorder logic, so this continues a recent line of work in which the Klm framework is lifted to more expressive languages.