K.J. Gilhooly (ed.)

Human and Machine Problem Solving

Herausgegeben von Gilhooly, K.J.

• Gebundenes Buch

Produktbeschreibung

Problem solving is a central topic for both cognitive psychology and artificial intelligence (AI). Psychology seeks to analyze naturally occur­ ring problem solving into hypothetical processes, while AI seeks to synthesize problem-solving performance from well-defined processes. Psychology may suggest possible processes to AI and, in turn, AI may suggest plausible hypotheses to psychology. It should be useful for both sides to have some idea of the other's contribution-hence this book, which brings together overviews of psychological and AI re­ search in major areas of problem solving. At a more general level, this book is intended to be a contribution toward comparative cognitive science. Cognitive science is the study of intelligent systems, whether natural or artificial, and treats both organ­ isms and computers as types of information-processing systems. Clearly, humans and typical current computers have rather different functional or cognitive architectures. Thus, insights into the role of cognitive ar­ chitecture in performance may be gained by comparing typical human problem solving with efficient machine problem solving over a range of tasks. Readers may notice that there is little mention of connectionist ap­ proaches in this volume. This is because, at the time of writing, such approaches have had little or no impact on research at the problem­ solving level. Should a similar volume be produced in ten years or so, of course, a very different story may need to be told.

---

Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.

Produktdetails

• Verlag: Springer, Berlin
• 1989.
• Seitenzahl: 400
• Erscheinungstermin: 31. Januar 1989
• Englisch
• Gewicht: 730g
• ISBN-13: 9780306429620
• ISBN-10: 0306429624
• Artikelnr.: 24545213

Inhaltsangabe

1 Human and Machine Problem Solving: Toward a Comparative Cognitive Science.- 1. Introduction.- 2. Problem Solving.- 2.1. Problems.- 2.2. Solving.- 3. Perspectives.- 3.1. Psychological Perspective.- 3.2. Machine Perspective.- 3.3. Interaction of Human and Machine Perspectives.- 4. Some Issues.- 5. References.- 2 Nonadversary Problem Solving by Machine.- 1. Introduction.- 1.1. Problem-Solving Systems.- 1.2. State Space Search and Problem Reduction.- 1.3. Blind Search and Heuristic Search.- 1.4. Graphs and Trees.- 2. State Space Representation.- 2.1. The Graph Traverser.- 2.2. Blind Search.- 2.3. Heuristic Search.- 3. Problem Reduction Representation: And/or Graphs.- 3.1. Blind Search.- 3.2. Heuristic Search.- 3.3. Means/Ends Analysis.- 4. Planning.- 4.1. Theorem-Proving Approaches.- 4.2. sTRips-like Systems.- 4.3. Hierarchical and Nonlinear Planners.- 5. Conclusions.- 6. References.- 3 Human Nonadversary Problem Solving.- 1. Introduction.- 1.1. Definitions.- 1.2. Types of Problems.- 1.3. Analysis of Problem Solving.- 2. Constraints on a Model of Human Nonadversary Problem Solving.- 2.1. Humans Systematically Distort the Problem To Be Consistent with Prior Knowledge.- 2.2. Humans Focus on Inappropriate Aspects of the Problem.- 2.3. Humans Change the Problem Representation during Problem Solving.- 2.4. Humans Apply Procedures Rigidly and Inappropriately.- 2.5. Humans Are Intuitive and Insightful and Creative.- 2.6. Humans Let Their Beliefs Guide Their Approach to Problem Solving.- 3. Conclusion.- 4. References.- 4 Adversary Problem Solving by Machine.- 1. Introduction.- 2. Search Techniques for Two-Person Games.- 3. Minimaxing with an Evaluation Function.- 4. The Alpha-Beta Algorithm.- 5. Refinements of the Basic Alpha-Beta Rule.- 6. Theoretical Analyses of Alpha-Beta and Its Variants.- 7. Other Problem-Independent Adversary Search Methods.- 8. Selective Search, Evaluation Functions, and Quiescence.- 9. A Short History of Game-Playing Programs.- 10. Example of Implementation Method for Chess.- 11. Knowledge-Based Selective Search.- 12. Exact Play in Chess Endgames.- 13. Other Nonprobabilistic Games.- 14. Games of Imperfect Information, Game Theory.- 15. Conclusion Likely Future Trends.- 16. References.- 17. Further Reading.- 5 Adversary Problem Solving by Humans.- 1. Adversary Games.- 1.1. Games Research.- 1.2. Memory and Skill.- 1.3. The Need for Alternative Explanations.- 2. Dealing with the Adversary.- 2.1. Predicting Opponent Moves.- 2.2. The Opponent s Intentions.- 3. Characteristics of the Search Process.- 3.1. Problem Behavior Graphs.- 3.2. Progress through the Tree.- 4. Plans and Knowledge.- 4.1. Using Plans.- 4.2. Using Knowledge.- 4.3. Knowledge and Skill.- 5. Evaluation Functions.- 5.1. Material and Positional Evaluations.- 5.2. Judgment and Skill.- 5.3. Comparison with Computers.- 6. Projecting Ahead.- 6.1. Following One Line of Moves.- 6.2. Anticipation through a Tree.- 6.3. Human Minimaxing.- 7. Humans versus Computers.- 7.1. Knowledge, Search, and Evaluation.- 7.2. Experimental Comparisons.- 7.3. Playing against Computers.- 8. Overview.- 8.1. Unresolved Issues.- 8.2. Conclusions.- 9. References.- 6 Machine Expertise.- 1. The Automation of Problem Solving Continuing a Tradition.- 2. Problem-Solving Knowledge Representation.- 3. The Nature of Expert Knowledge.- 4. Knowledge Representation.- 5. Problems with the Traditional Approach.- 6. Architectures for Representing Machine Expertise.- 6.1. The Production System Approach.- 6.2. Multiple Experts and Mixed Reasoning Strategies.- 6.3. The Set-Covering Approach (or Frame Abduction).- 6.4. Multiple Paradigms.- 7. The Rule-Based Approach mycin, prospector, and xcon.- 7.1. The mycin System.- 7.2. The xcon System (r1).- 7.3. The prospector System.- 8. The Blackboard Approach (hearsay).- 9. The Set-Covering Approach (Frame Abduction).- 9.1. The Inference Mechanism.- 9.2. System D An Example.- 9.3. The internist System.- 10. Multiple Paradigm Approaches.- 10.1. The compass S