再贴一本Expert System

laotao · 发表于 2006-9-8 23:14:58

读完大半本了，真的是好书，下面的目录是俺一个字一个字打上去的，不过刚刚发现当当上有现成的：（Expert systems principles and programming Contents

reface Foreword to the first edition 1 Chapter1: introduction to expert systems 1.1 Introduction 1.2 What is an expert system 1.3 Advantages of expert systems 1.4 General concepts of expert systems 1.5 Characteristics of an expert system 1.6 The development of expert systems technology 1.7 Expert systems applications and domains 1.8 Languages, shells, and tools 1.9 Elements of an expert system 1.10 Production systems 1.11 Procedural paradigms 1.12 Nonprocedural paradigms 1.13 Artificial neural systems 1.14 Connectionist expert systems and inductive learning 1.15 Summary 2 Chapter 2: the representation of knowledge 2.1 Introduction 2.2 The meaning of knowledge 2.3 Productions 2.4 Semantic nets 2.5 Object-attribute-value triples 2.6 PROLOG and semantic nets 2.7 Difficulties with semantic nets 2.8 Schemata 2.9 Frames 2.10 Difficulties with frames 2.11 Logic and sets 2.12 Propositional logic 2.13 The first order predicate logic 2.14 The universal quantifier 2.15 The existential quantifier 2.16 Quantifiers and sets 2.17 Limitations of predicate logic 2.18 Summary 3 Chapter 3: methods of inference 3.1 Introduction 3.2 Trees, lattices, and graphs 3.3 State and problem spaces 3.4 And-or trees and goals 3.5 Deductive logic and syllogisms 3.6 Rules of inference 3.7 Limitations of propositional logic 3.8 First order predicate logic 3.9 Logic systems 3.10 Resolution 3.11 Resolution systems and deduction 3.12 Shallow and causal reasoning 3.13 Resolution and first order predicate logic 3.14 Forward and backward chaining 3.15 Metaknowledge 3.16 Summary 4 Chapter 4: reasoning under uncertainty 4.1 Introduction 4.2 Uncertainty 4.3 Types of error 4.4 Errors and induction 4.5 Classical probability 4.6 Experimental and subjective probabilities 4.7 Compound probabilities 4.8 Conditional probabilities 4.9 Hypothetical reasoning and backward induction 4.10 Temporal reasoning and Markov chains 4.11 The odds of belief 4.12 Sufficiency and necessity 4.13 Uncertainty in inference chains 4.14 The combination of evidence 4.15 Inference nets 4.16 The propagation of probabilities 4.17 Summary 5 Chapter 4: inexact reasoning 5.1 Introduction 5.2 Uncertainty and rules 5.3 Certainty factors 5.4 Dempster-Shafer theory 5.5 Approximate reasoning 5.6 The state of uncertainty 5.7 Summary 6 Chapter 6: design of expert systems 6.1 Introduction 6.2 Selecting the appropriate problem 6.3 Stages in the development of and expert system 6.4 Errors in development stages 6.5 Software engineering and expert systems 6.6 The expert system life cycle 6.7 A detailed life cycle model 6.8 Summary 7 Chapter 7: introduction to CLIPS 7.1 Introduction 7.2 CLIPS 7.3 Notation 7.4 Fields 7.5 Entering and exiting CLIPS 7.6 Facts 7.7 Adding and removing facts 7.8 Modifying and duplicating facts 7.9 The watch command 7.10 The deffacts construct 7.11 The components of a rule 7.12 The agenda and execution 7.13 Commands for manipulating constructs 7.14 The printout command 7.15 Using multiple rules 7.16 The set-break command 7.17 Loading and saving constructs 7.18 Commenting constructs 7.19 Summary 8 Pattern matching 9 Advanced pattern matching 10 Modular design and execution control 11 Efficiency in rule-based languages 12 Expert system design examples Appendix a: some useful equivalences Appendix b: some elementary quantifiers and their meanings Appendix c: some set properties Appendix d: clips support information Appendix e: clips command and function summary Appendix f: clips BNF Index

reface Expert systems have experience tremendous growth and popularity since their commercial introduction in the early 1980s. Today, expert systems are used in business, science, engineering, manufacturing, and many other fields. This book is meant to educate Foreword to the first edition When AI was born in the mid-fifties, its primary concerns were centered on game playing, planning, and problem solving. In the environment of that era, it would have been very difficult to predict that three decades later the most important application areas of AI would be centered on knowledge engineering and, more particularly, on expert system. Expert systems as we know them today have their roots in the pioneering work of Feigenbaum, Lederberg, Shortliffe, and Buchanan at Stanford University in the late sixties and early seventies. What is remarkable about this work—and MYCIN in particular—is that it still serves as a paradigm for much of the current activity in expert and, more generally, knowledge-based systems. During the past few years, demonstrable successes of expert systems in specialized application areas such as medical diagnosis and computer system configuration have led to an explosion of interest in a much wider variety of applications, ranging from battle plan management and optimal routing to fault diagnosis, optimal portfolio selection, and the assessment of creditworthiness of loan applicants. The mushrooming of applications has generated two distinct needs: (a) the development of a better understanding of how to deal with the key problems of knowledge representation, inference, and uncertainty management; and (b) the development of expert system shells and/or programming languages which minimize the effort needed for constructing a knowledge representation system and an inference engine for a particular application. The book written by Joseph Giarratano and Gary Riley is the only book Chapter 1 Introduction to expert systems 1.1 introduction 1.2 what is an expert system? The first step in solving any problem is defining the problem area or domain to be solved. This consideration is just as true in artificial intelligence (AI) as in conventional programming. However, because of the mystique formerly associated with AI, there is a lingering tendency to still believe the old adage “It’s an AI problem if it hasn’t been solved yet.” Another popular definition is that “AI is making computers act like they do in the movies.” This type of mind set may have been popular in the 1970s when AI was entirely in a research stage. However, today there are many real-world problems that are being solved by AI and many commercial applications of AI. Although general solutions to classic AI problems such as natural language translation, speech understanding, and vision have not been found, restricting the problem domain may still produce a useful solution. For example, it is not difficult to build simple natural language systems if the input is restricted to sentences of the form noun, verb, and object. Currently, systems of this type work well in providing a user-friendly interface to many software products such as database systems and spreadsheets. In fact, the parsers associated with popular computer text-adventure games today exhibit an amazing degree of ability in understanding natural language. As figure 1.1 shows, AI has many areas of interest. The area of expert systems is a very successful approximate solution to the classic AI problem of programming intelligence. Professor Edward Feigenbaum of Stanford University, an early pioneer of expert systems technology, has define an expert system as “ an intelligent computer program that uses knowledge and inference procedures to solve problems that are difficult enough to require significant human expertise for their solutions.” That is, an expert system is a computer system that emulates the decision-making ability of a human expert. The term emulate means that the expert system is intended to act in all respects like a human expert. An emulation is much stronger than a simulation, which is only required to act like the real thing in some respects. <div>此帖由 laotao 在 2006-09-08 23:19 进行编辑...</div>

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