Design of logic-based intelligent systems

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Title:

Personal Author:

Truemper, Klaus

Publication Information:

Hoboken, N.J. : John Wiley & Sons, 2004

ISBN:

9780471484035

Subject Term:

Expert systems (Computer science)

Artificial intelligence

Available:*

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010060065	QA76.76.E95 T78 2004	Open Access Book	Book	Searching... Unknown
Searching... PSZ KL	30000010074117	QA76.76.E95 T78 2004	Open Access Book	Book	Searching... Unknown
Searching... PSZ KL	30000010081522	QA76.76.E95 T78 2004	Open Access Book	Book	Searching... Unknown

Principles for constructing intelligent systems
Design of Logic-based Intelligent Systems develops principles and methods for constructing intelligent systems for complex tasks that are readily done by humans but are difficult for machines. Current Artificial Intelligence (AI) approaches rely on various constructs and methods (production rules, neural nets, support vector machines, fuzzy logic, Bayesian networks, etc.). In contrast, this book uses an extension of propositional logic that treats all aspects of intelligent systems in a unified and mathematically compatible manner.
Topics include:
* Levels of thinking and logic
* Special cases: expert systems and intelligent agents
* Formulating and solving logic systems
* Reasoning under uncertainty
* Learning logic formulas from data
* Nonmonotonic and incomplete reasoning
* Question-and-answer processes
* Intelligent systems that construct intelligent systems
Design of Logic-based Intelligent Systems is both a handbook for the AI practitioner and a textbook for advanced undergraduate and graduate courses on intelligent systems. Included are more than forty algorithms, and numerous examples and exercises. The purchaser of the book may obtain an accompanying software package (Leibniz System) free of charge via the internet at leibnizsystem.com.

Author Notes

Klaus Truemper is Professor of Computer Science at the University of Texas at Dallas.

Reviews 1

Choice Review

Truemper (Univ. of Texas, Dallas) provides an in-depth look at the foundation of prepositional logic and advanced systems built using the principles of logic. The area of prepositional logic forms the very foundation of artificial intelligence (AI) and has been the cornerstone tool for AI for decades. Although there are many modern tools that have been developed recently, there is still significant interest in the use of prepositional logic. Truemper offers definitive, state-of-the-art coverage of this area. First, the foundational material on prepositional logic is presented, followed by the techniques on logic systems; then learning strategies and advanced reasoning are discussed. Finally, a number of applications of prepositional logic systems are highlighted. This book differs from other works in that the author stresses the rationale and reasoning in anecdotal discussions rather than through the rigor of proofs. Hence, the presented material is easy to read and follow. Appropriate for college seniors and first-year graduate students. ^BSumming Up: Recommended. Upper-division undergraduates; graduate students. J. Y. Cheung University of Oklahoma

Preface	p. xi
Chapter 1 Introduction	p. 1
1.1 Overview	p. 1
1.2 Terminology	p. 2
1.3 Levels of Thinking	p. 3
1.4 Logic Tools	p. 4
1.5 Formulation of Models	p. 6
1.6 Computational Complexity	p. 7
1.7 Software	p. 7
1.8 Suggested Reading Sequences	p. 8
Part I Logic Problems	p. 9
Chapter 2 Introduction to Logic and Problems SAT and MINSAT	p. 11
2.1 Overview	p. 11
2.2 Propositional Logic	p. 12
2.3 First-order Logic	p. 18
2.4 Classification of Propositional Formulas	p. 20
2.5 Theorem Proving and Decision Making	p. 21
2.6 Logic Minimization	p. 25
2.7 Other Kinds of Logic	p. 26
2.8 Further Reading	p. 29
2.9 Exercises	p. 30
Chapter 3 Variations of SAT and MINSAT	p. 34
3.1 Overview	p. 34
3.2 Problem MAXCLS SAT	p. 36
3.3 Problem MINCLS UNSAT	p. 39
3.4 Problem MAXVAR SAT	p. 42
3.5 Problem MINVAR UNSAT	p. 44
3.6 Problem MAXSAT	p. 47
3.7 Further Reading	p. 51
3.8 Exercises	p. 51
Chapter 4 Quantified SAT and MINSAT	p. 55
4.1 Overview	p. 55
4.2 Problem Q-ALL SAT	p. 58
4.3 Problem Q-MIN UNSAT	p. 61
4.4 Problem Q-MAX MINSAT	p. 66
4.5 More Complicated Quantified Problems	p. 70
4.6 Heuristic Algorithms	p. 77
4.7 Further Reading	p. 87
4.8 Exercises	p. 87
Part II Formulation of Logic Systems	p. 95
Chapter 5 Basic Formulation Techniques	p. 97
5.1 Overview	p. 97
5.2 Variables and Clauses	p. 98
5.3 Redundant Clauses	p. 100
5.4 Inconsistent Clauses	p. 102
5.5 Validation	p. 104
5.6 Decision Pyramid	p. 108
5.7 Explanations	p. 111
5.8 Accelerated Theorem Proving	p. 116
5.9 Decision Graphs	p. 120
5.10 Difficult Cases	p. 126
5.11 Further Reading	p. 128
5.12 Exercises	p. 129
Chapter 6 Uncertainty	p. 132
6.1 Overview	p. 132
6.2 Basic Rule	p. 134
6.3 Satisfiability	p. 136
6.4 Minimum Cost Satisfiability	p. 141
6.5 Quantified SAT and MINSAT	p. 147
6.6 Defuzzification	p. 148
6.7 Further Reading	p. 151
6.8 Exercises	p. 152
Part III Learning	p. 155
Chapter 7 Learning Formulas	p. 157
7.1 Overview	p. 157
7.2 Basic Concepts	p. 158
7.3 Separation of Two Sets	p. 167
7.4 Min and Max Formulas	p. 175
7.5 Optimized Formulas	p. 178
7.6 Additional Logic Constraints	p. 188
7.7 Reversing the Roles of Sets	p. 189
7.8 Voting	p. 192
7.9 Further Reading	p. 193
7.10 Exercises	p. 193
Chapter 8 Accuracy of Learned Formulas	p. 197
8.1 Overview	p. 197
8.2 Subsets of Training Data	p. 199
8.3 Logic Formulas for Subsets	p. 202
8.4 Classification Errors	p. 204
8.5 Vote Distributions	p. 206
8.6 Classification Control	p. 209
8.7 Multipopulation Classification	p. 213
8.8 Further Reading	p. 227
8.9 Exercises	p. 228
Part IV Advanced Reasoning	p. 233
Chapter 9 Nonmonotonic and Incomplete Reasoning	p. 235
9.1 Overview	p. 235
9.2 Nonmonotonicity	p. 238
9.3 Incompleteness	p. 246
9.4 Uncertain Nonmonotonicity and Incompleteness	p. 250
9.5 Further Reading	p. 254
9.6 Exercises	p. 254
Chapter 10 Question-and-Answer Processes	p. 256
10.1 Overview	p. 256
10.2 Basic Process	p. 258
10.3 Definitions	p. 260
10.4 Reduction of CNF System	p. 263
10.5 Proof of Conclusions	p. 265
10.6 Selection of Goal Set	p. 268
10.7 Low-cost Assignments	p. 268
10.8 Selection of Tests	p. 272
10.9 QA Process	p. 278
10.10 Explanations	p. 280
10.11 Variation: Optimization	p. 281
10.12 Evaluation of Learned Formulas	p. 287
10.13 Further Reading	p. 293
10.14 Exercises	p. 294
Part V Applications	p. 299
Chapter 11 Applications	p. 301
11.1 Overview	p. 301
11.2 Correctness of Design	p. 302
11.3 Music Composition Assistant	p. 310
11.4 Management of Hazardous Materials	p. 314
11.5 Traffic Control	p. 316
11.6 Credit Rating	p. 320
11.7 Deciding Word Sense	p. 324
11.8 Differential Medical Diagnosis	p. 329

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