A Proof and Some Representations

Hilbert defined proofs as derivations from axioms via the modus ponens rule and variable instantiation (this definition has a certain parallel to the ‘recognise-act cycle’ in artificial intelligence). A pre-defined set of rules is applied to an initial state until a goal state is reached. Although this definition is very powerful and it can be argued that nothing else is needed, the nature of proof turns out to be much more diverse, for instance, changes in representation are frequently done. We will explore some aspects of this by McCarthy’s ‘mutilated checkerboard’ problem and discuss the tension between the complexity and the power of mechanisms for finding proofs.

[1]  Manfred Kerber,et al.  A Tough Nut for Mathematical Knowledge Management , 2005, MKM.

[2]  William McCune,et al.  Solution of the Robbins Problem , 1997, Journal of Automated Reasoning.

[3]  Jacques Herbrand Recherches sur la théorie de la démonstration , 1930 .

[4]  J. McCarthy A Tough Nut for Proof Procedures , 1964 .

[5]  Michael Wooldridge,et al.  Foundations of Rational Agency , 1999 .

[6]  René Descartes,et al.  Discours de la Methode , 2013 .

[7]  John McCarthy The Mutilated Checkerboard in Set Theory , 2006 .

[8]  J. A. Robinson,et al.  A Machine-Oriented Logic Based on the Resolution Principle , 1965, JACM.

[9]  I. Lakatos,et al.  Proofs and Refutations: Frontmatter , 1976 .

[10]  Godfrey H. Hardy,et al.  A mathematician's apology , 1941 .

[11]  R. Feynman Surely You''re Joking Mr , 1992 .

[12]  V. Tamari Surely You're Joking, Mr. Feynman! , 1985 .

[13]  A. Ayer,et al.  Language, Truth and Logic. , 1939 .

[14]  A. Bundy,et al.  What is a proof? , 2005, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[15]  Christoph Benzmüller,et al.  Automatic Learning of Proof Methods in Proof Planning , 2003, Log. J. IGPL.

[16]  I. Lakatos PROOFS AND REFUTATIONS (I)*† , 1963, The British Journal for the Philosophy of Science.