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Applying Lakatos-Style Reasoning to AI Domains
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Author(s): Alison Pease (University of Edinburgh, United Kingdom), Andrew Ireland (Heriot-Watt University, United Kingdom), Simon Colton (Imperial College London, United Kingdom), Ramin Ramezani (Imperial College London, United Kingdom), Alan Smaill (University of Edinburgh, United Kingdom), Maria Teresa Llano (Heriot-Watt University, United Kingdom), Gudmund Grov (University of Edinburgh, United Kingdom)and Markus Guhe (University of Edinburgh, United Kingdom)
Copyright: 2010
Pages: 25
Source title:
Thinking Machines and the Philosophy of Computer Science: Concepts and Principles
Source Author(s)/Editor(s): Jordi Vallverdú (Universitat Autònoma de Barcelona, Spain)
DOI: 10.4018/978-1-61692-014-2.ch010
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Abstract
One current direction in AI research is to focus on combining 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 we apply these heuristics to the AI domains of evolving requirement specifications, planning and constraint satisfaction problems. In drawing analogies between Lakatos’s theory and these three domains we 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, we show how we might begin to produce a philosophically-inspired AI theory of combined reasoning.
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