KLM-Style Defeasible Reasoning for Datalog

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dc.contributor.advisorMeyer, Thomas
dc.contributor.advisorCasini, Giovanni
dc.contributor.authorPaterson-Jones, Guy
dc.date.accessioned2023-04-14T08:57:09Z
dc.date.available2023-04-14T08:57:09Z
dc.date.issued2022
dc.date.updated2023-04-14T07:45:39Z
dc.description.abstractIn 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.
dc.identifier.apacitationPaterson-Jones, G. (2022). <i>KLM-Style Defeasible Reasoning for Datalog</i>. (). ,Faculty of Science ,Department of Computer Science. Retrieved from http://hdl.handle.net/11427/37738en_ZA
dc.identifier.chicagocitationPaterson-Jones, Guy. <i>"KLM-Style Defeasible Reasoning for Datalog."</i> ., ,Faculty of Science ,Department of Computer Science, 2022. http://hdl.handle.net/11427/37738en_ZA
dc.identifier.citationPaterson-Jones, G. 2022. KLM-Style Defeasible Reasoning for Datalog. . ,Faculty of Science ,Department of Computer Science. http://hdl.handle.net/11427/37738en_ZA
dc.identifier.ris TY - Master Thesis AU - Paterson-Jones, Guy AB - 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. DA - 2022_ DB - OpenUCT DP - University of Cape Town KW - Computer Science LK - https://open.uct.ac.za PY - 2022 T1 - KLM-Style Defeasible Reasoning for Datalog TI - KLM-Style Defeasible Reasoning for Datalog UR - http://hdl.handle.net/11427/37738 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/37738
dc.identifier.vancouvercitationPaterson-Jones G. KLM-Style Defeasible Reasoning for Datalog. []. ,Faculty of Science ,Department of Computer Science, 2022 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/37738en_ZA
dc.language.rfc3066eng
dc.publisher.departmentDepartment of Computer Science
dc.publisher.facultyFaculty of Science
dc.subjectComputer Science
dc.titleKLM-Style Defeasible Reasoning for Datalog
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationlevelMSc
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