The Logic of Learning: A Basis for Pattern Recognition and for Improvement of Performance

Publisher Summary This chapter discusses the logic of learning. It defines the phenomenon of pattern recognition. The two initial concepts in a theory of pattern recognition are illustrated by the set of states of a problem and the subsets of it required by the search reduction techniques. In considerations of language, one can describe certain subsets of the universe other than the elementary concepts: any logical statement involving the names of the elementary concepts defines subsets of the universe. Moreover, one can define relations between objects by specifying that the two satisfy two related statements. The chapter classifies the programs along a number of different parameters. One will be according to the language used. There are two aspects to this: One is the nature of the atoms in the language, that is, whether the predicates take only one variable (and thus define classes) or can also define relations by using more than one variable. The other aspect that distinguishes the languages used in different programs is in the nature of the Horn clauses themselves. The chapter concludes that the study of learning has been directed to specific tasks and accordingly many basic problems have been clarified. As this understanding deepens, the field, likened to artificial intelligence, will develop into two branches, applied and pure.