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This book began as a series of lecture notes for a course called Introduc tion to Adaptive Systems which I developed for undergraduate Computing Science majors at the University of Alberta and first taught in 1973. The objective of the course has been threefold: (l) to expose undergraduate computer scientists to a variety of subjects in the theory and application of computation, subjects which are too often postponed to the graduate level or never taught at all; (2) to provide undergraduates with a background sufficient to make them effective participants in graduate level courses in Automata Theory, Biological Information Processing, and Artificial Intelligence; and (3) to present a personal viewpoint which unifies the apparently diverse aspects of the subject matter covered. All of these goals apply equally to this book, which is primarily designed for use in a one semester undergraduate computer science course. I assume the reader has a general knowledge of computers and programming, though not of particular machines or languages. His mathematical background should include basic concepts of number systems, set theory, elementary discrete probability, and logic.
List of contents
I Information and automata.- 1 Communication theory.- 2 Coding information.- 3 Finite automata.- 4 Turing machines.- 5 Cellular automata.- II Biological information processing.- 6 Biochemical coding and control.- 7 Genetic information transmission.- 8 Neural information transmission.- 9 Neural input-output.- 10 Computer simulation models.- III Artificial intelligence.- 11 Pattern recognition.- 12 Game playing.- 13 Theorem proving.- 14 Problem solving.- 15 Natural language processing.
