Problem solving : methods, programming, and future concepts /

Problem solving is the very area of articifical intelligence AI which, probably, will never result in a complete set of formalized theories, in a pragmatic philosophy, or in a "universal" applied discipline. Studying questions concerning this area, encompasses different concepts, models an...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: German, O. V. (Oleg Vitol�dovich)
Otros Autores: Ofitserov, Dmitri V.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam ; New York : Elsevier, 1995.
Colección:Studies in computer science and artificial intelligence ; 12.
Temas:
Artificial intelligence.
Problem solving.
Artificial Intelligence
Problem Solving
Intelligence artificielle.
R�esolution de probl�eme.
artificial intelligence.
COMPUTERS > Enterprise Applications > Business Intelligence Tools.
COMPUTERS > Intelligence (AI) & Semantics.
Artificial intelligence
Problem solving
Acceso en línea:Texto completo
Texto completo
Tabla de Contenidos:
  • Cover
  • CONTENTS
  • PREFACE
  • CONTENTS
  • INTRODUCTION
  • Conception of the book
  • The history of the subject
  • State of the art
  • CHAPTER 0. PROBLEM CLASSIFICATION. INTRODUCTION TO THE SOLVING METHODS
  • 0.1 What is a problem?
  • 0.2 Problem classification
  • 0.3 An approach to building an interpretation calculus
  • 0.4 Finding a solution by means of theorem proving
  • 0.5 Finding an optimum interpretation
  • 0.6 Psychological aspects
  • 0.7 Conclusion
  • CHAPTER 1. ELEMENTS OF PROBLEM SOLVING THEORY: APPLICATION OF CUTTING STRATEGIES
  • 1.1 Introduction
  • 1.2 The properties of a solution's elements
  • 1.3 A system of axioms for incompatibility calculus
  • 1.4 An algorithm for searching for maximum-size zero submatrix
  • 1.5 On the minimum-size cover problem (MSCP)
  • 1.6 Precedence and incompatibility
  • 1.7 Prohibition
  • 1.8 Conditional executability
  • 1.9 Other examples
  • 1.10 Conclusion
  • CHAPTER 2. SOLVING DISCRETE OPTIMIZATION PROBLEMS ON THE BASIS OF?-TRANSFORM METHOD
  • 2.1?-transform method
  • 2.2 Some important cases of the analytical representation of the?(q)-function
  • 2.3 A general scheme for the discrete? -transform method
  • 2.4 An approximate solution to F-indefinite static optimization problems
  • 2.5 Conclusion
  • CHAPTER 3. WEAK METHODS AND HEURISTIC REASONING
  • 3.1 Specific features of solving tasks by weak methods
  • 3.2 Control of the solving process
  • 3.3 Models of heuristic-based solution searching
  • 3.4 Try-and-test procedures with cutting
  • 3.5 Intermediate remarks on heuristics utilization
  • 3.6 Examples of problem solving principles
  • 3.7 Solution tree
  • 3.8 Principle of dominance and choice function
  • 3.9 An example of mechanization of heuristics
  • 3.10 Conclusion
  • CHAPTER 4. LOGIC-BASED PROBLEM SOLVERS: APPROACHES AND NEW METHODS
  • 4.1 Introduction
  • 4.2 Logical problem solvers
  • 4.3 Group resolution principle in predicate calculus
  • 4.4 Implementation of group resolution principle
  • 4.5 Reduction algorithm with term re-writing
  • 4.6 Conclusion
  • CHAPTER 5. PROGRAMMING CONCEPTS IN PROBLEM SOLVING
  • 5.1. Programming or theorem proving?
  • 5.2 Universal algorithm paradigm
  • 5.3 Computer mathematics
  • 5.4 Expert systems
  • 5.5 Evolutionary problem solution synthesis (EPSS) concept
  • 5.6 Mathematical induction and pattern recognition approaches
  • 5.7 Intellectual support concept in the problem solving system
  • 5.8 Making a semantic structure of the problem
  • 5.9 Conclusion
  • CHAPTER 6. FUTURE CONCEPTS: SOME PHILOSOPHICAL ISSUES
  • 6.1 Universal problem solving approach restoration
  • 6.2 Weak methods become strong
  • 6.3 The role of formal logic in future developments
  • 6.4 The human factor
  • 6.5 Are there other paradigms?
  • 6.6 Conclusive remarks
  • REFERENCES
  • GLOSSARY
  • INDEX
  • Last Page.

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