Adaptive Information Processing

1. Front Matter

   Pages i-x

   PDF

2. Introduction

   1. Introduction

      • Jeffrey R. Sampson

      Pages 1-3

3. Information and automata

   1. Front Matter

      Pages 5-6

      PDF

   2. Communication theory

      • Jeffrey R. Sampson

      Pages 7-15

   3. Coding information

      • Jeffrey R. Sampson

      Pages 16-24

   4. Finite automata

      • Jeffrey R. Sampson

      Pages 25-39

   5. Turing machines

      • Jeffrey R. Sampson

      Pages 40-55

   6. Cellular automata

      • Jeffrey R. Sampson

      Pages 56-66

4. Biological Information Processing

   1. Front Matter

      Pages 67-68

      PDF

   2. Biochemical coding and control

      • Jeffrey R. Sampson

      Pages 69-84

   3. Genetic information transmission

      • Jeffrey R. Sampson

      Pages 85-95

   4. Neural information transmission

      • Jeffrey R. Sampson

      Pages 96-106

   5. Neural input—output

      • Jeffrey R. Sampson

      Pages 107-117

   6. Computer simulation models

      • Jeffrey R. Sampson

      Pages 118-126

5. Artificial intelligence

   1. Front Matter

      Pages 127-128

      PDF

   2. Pattern recognition

      • Jeffrey R. Sampson

      Pages 129-145

   3. Game playing

      • Jeffrey R. Sampson

      Pages 146-159

   4. Theorem proving

      • Jeffrey R. Sampson

      Pages 160-174

   5. Problem solving

      • Jeffrey R. Sampson

      Pages 175-189

   6. Natural language processing

      • Jeffrey R. Sampson

      Pages 190-207

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.

• Information
• Processing
• artificial intelligence
• automata
• computer
• computer science
• probability
• programming

• Department of Computing Science, The University of Alberta, Edmonton, Canada

  Jeffrey R. Sampson

Policies and ethics