Part I: Artificial Intelligence: Its Routes and Scope 1 AI: history and applications 1.0 From Eden to ENIAC: Attitudes toward intelligence, knowledge and human artifice 1.1 Overview of AI application areas 1.2 Artificial intelligence - a summary 1.3 Epilogue and references 1.4 Exercises Part II: Artificial Intelligence as Representation and Search 2 The predicate calculus 2.0 Introduction 2.1 The propositional calculus 2.

2 The predicate calculus 2.3 Using inference rules to produce predicate calculus expressions 2.4 Application: a logic-based financial advisor 2.5 Epilogue and references 2.6 Exercises 3 Structures and Strategies for State Space Search 3.0 Introduction 3.1 Graph theory 3.2 Strategies for state space search 3.

3 Using the state space to represent reasoning with predicate calculus 3.4 Epilogue and references 3.5 Exercises 4 Heuristic Search 4.0 Introduction 4.1 An algorithm for heuristic search 4.2 Admissibility, monotonicity, and informedness 4.3 Using heuristics in games 4.4 Complexity issues 4.

5 Epilogue and references 4.6 Exercises 5 Control and Implementation of State Space Search 5.0 Introduction 5.1 Recursion-based search 5.2 Pattern-directed search 5.3 Production systems 5.4 The blackboard architecture for problem solving 5.5 Epilogue and references 5.

6 Exercises Part III: Representation and Intelligence: The AI Challenge 6 Knowledge Representation 6.0 Issues in knowledge representation 6.1 A brief history of AI representational systems 6.2 Comceptual graphs: a network language 6.3 Alternatives to explicit representation 6.4 Agent based and distributed problem solving 6.5 Epilogue and references 6.6 Exercises 7 Strong Method Problem Solving 7.

0 Introduction 7.1 Overview of expert systems technology 7.2 Rule-based expert systems 7.3 Model-based, case based, and hybrid systems 7.4 Planning 7.5 Epilogue and references 7.6 Exercises 8 Reasoning in Uncertain Situations 8.0 Introduction 8.

1 Logic-based abductive inference 8.2 Abduction: alternatives to logic 8.3 The stochastic approach to uncertainty 8.4 Epilogue and references 8.5 Exercises Part IV: Machine Learning 9 Machine Learning: Symbol-Based 9.0 Introduction 9.1 A framework for symbol-based learning 9.2 Version space search 9.

3 The ID3 decision tree induction algorithm 9.4 Inductive bias and learnability 9.5 Knowledge and learning 9.6 Unsupervised learning 9.7 Reinforcement learning 9.8 Epilogue and references 9.9 Exercises 10 Machine Learning: Connectionist 10.0 Introduction 10.

1 Foundations for connectionist networks 10.2 Perceptron learning 10.3 Backpropagation learning 10.4 Competitive learning 10.5 Hebbian coincidence learning 10.6 Attractor networks or ''memoroes'' 10.7 Epilogue and references 10.8 Exercises 11 Machine Learning: Social and Emergent 11.

0 Social and emergent models of learning 11.1 The genetic algorithm 11.2 Classifier systems and genetic programming 11.3 Artificial life and society-based learning 11.4 Epilogue and references 11.5 Exercises Part V: Advanced Topics for AI Problem Solving 12 Automated Reasoning 12.0 Introduction to weak methods in theorem proving 12.1 The general problem solver and difference tables 12.

2 Resolution theorem proving 12.3 PROLOG and automated reasoning 12.4 Further issues in automated reasoning 12.5 Epilogue and references 12.6 Exercises 13 Understanding Natural Language 13.0 Role of knowledge in language understanding 13.1 Deconstructing language: a symbolic analysis 13.2 Syntax 13.

3 Syntax and knowledge with ATN parsers 13.4 Stochastic tools for language analysis 13.5 Natural language applications 13.6 Epilogue and references 13.7 Exercises Part VI: Languages and Programming Techniques for Artificial Intelligence 14 An Introduction to PROLOG 14.0 Introduction 14.1 Syntax for predicate calculus programming 14.2 Abstract data types (ADTs) in PROLOG 14.

3 A production system example in PROLOG 14.4 Designing alternative search strategies 14.5 A PROLOG planner 14.6 PROLOG: meta-predicates, types, and unification 14.7 Meta-interpreters in PROLOG 14.8 Learning algorithms in PROLOG 14.9 Natural language processing in PROLOG 14.10 Epilogue and references 14.

11 Exercises 15 An Introduction to LISP 15.0 Introduction 15.1 LISP: a brief overview 15.2 Search in LISP: a functional approach to the farmer, wolf, goat, and cabbage problem 15.3 Higher-order functions and procedural abstraction 15.4 Search strategies in LISP 15.5 Pattern matching in LISP 15.6 A recursive unification function 15.

7 Interpreters and embedded languages 15.8 Logic programming in LISP 15.9 Streams and delayed evaluation 15.10 An expert system shell in LISP 15.11 Semantic networks and inheritance in LISP 15.12 Object-oriented programming using CLOS 15.13 Learning in LISP: the ID3 algorithm 15.14 Epilogue and references 15.

15 Exercises Part VII: Epilogue 16 Artificial Intelligence as Empirical Enquiry 16.0 Introduction 16.1 Artificial intelligence: a revised definition 16.2 The science of intelligent systems 16.3 AI: current issues and future directions 16.4 Epilogue and references Bibliography Author Index Subject Index.