Applying lakatos-style reasoning to AI problems

Alison Pease, Andrew Ireland, Simon Colton, Ramin Ramezani, Alan Smaill, Maria Teresa Llano, Gudmund Grov, Markus Guhe

Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Researchpeer-review

5 Citations (Scopus)


One current direction in AI research is to combine different reasoning styles such as deduction, induction, abduction, analogical reasoning, non-monotonic reasoning, vague and uncertain reasoning. The philosopher Imre Lakatos produced one such theory of how people with different reasoning styles collaborate to develop mathematical ideas. Lakatos argued that mathematics is a quasi-empirical, flexible, fallible, human endeavour, involving negotiations, mistakes, vague concept definitions and disagreements, and he outlined a heuristic approach towards the subject. In this chapter the authors apply these heuristics to the AI domains of evolving requirements specifications, planning and constraint satisfaction problems. In drawing analogies between Lakatos's theory and these three domains they identify areas of work which correspond to each heuristic, and suggest extensions and further ways in which Lakatos's philosophy can inform AI problem solving. Thus, the authors show how they might begin to produce a philosophically-inspired theory of combined reasoning in AI.

Original languageEnglish
Title of host publicationThinking Machines and the Philosophy of Computer Science
Subtitle of host publicationConcepts and Principles
PublisherIGI Global
Number of pages25
ISBN (Print)9781616920142
Publication statusPublished - 1 Dec 2010
Externally publishedYes

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