7 The Generalized Resolution Principle
–AI Classics/files/AI/classics/Machine Intelligence 3/MI3-Ch.7-Robinson.pdf
It is a natural generalization of the various versions and extensions of the resolution principle, each of which it includes as special cases; but in addition it supplies all of the inferential machinery which is needed in order to be able to treat the intended interpretation of the equality symbol as'built in', and obviates the need to include special axioms of equality in the formulation of every theorem-proving problem which makes use of that notion. The completeness theory of the generalized resolution principle exploits the very intuitive and natural idea of attempting to construct counterexamples to the theorems for which proofs are wanted, and makes this the central concept. It is shown how a proof of a theorem is generated automatically by the breakdown of a sustained attempt to construct a counterexample for it. The kind of proof one gets depends entirely on the way in which the attempt to construct a counterexample is organized, and the theory places virtually no restrictions on how this shall be done. Consequently there is a very wide freedom in the form which proofs may take: the individual inferences in a proof may be very'small' or very'large' (in a scale of measurement which, roughly speaking, weighs the amount of computing necessary to check that the inference is correct). It is even correct to infer the truth of a true proposition in just one step, but, presumably, to offer such a proof to someone who wishes to be convinced of the proposition's truth would not be helpful epistemologically. His conviction would come, not from contemplating the proof itself, but rather from examining the computation which shows the correctness of its single inference step.
Jan-25-2015, 22:14:13 GMT