SCADR

Knowledge representation and reasoning (KRR) is an approach to artificial intelligence (AI) in which a system has some information about the world represented formally (a knowledge base), and is able to reason about this information. Defeasible reasoning is a non-classical form of reasoning that enables systems to reason about knowledge bases which contain seemingly contradictory information, thus allowing for exceptions to assertions. Currently, systems which support defeasible entailment for propositional logic are ad hoc, and few and far between, and little to no work has been done on improving the scalability of defeasible reasoning algorithms. We investigate the scalability of defeasible entailment algorithms, and propose optimised versions thereof, as well as present a tool to perform defeasible entailment checks using these algorithms. We also present a knowledge base generation tool which can be used for testing implementations of these algorithms.