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…mehr
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 intothe 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.
1 Human and Machine Problem Solving: Toward a Comparative Cognitive Science.- 1. Introduction.- 2. Problem Solving.- 3. Perspectives.- 4. Some Issues.- 5. References.- 2 Nonadversary Problem Solving by Machine.- 1. Introduction.- 2. State Space Representation.- 3. Problem Reduction Representation: And/or Graphs.- 4. Planning.- 5. Conclusions.- 6. References.- 3 Human Nonadversary Problem Solving.- 1. Introduction.- 2. Constraints on a Model of Human Nonadversary 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.- 2. Dealing with the Adversary.- 3. Characteristics of the Search Process.- 4. Plans and Knowledge.- 5. Evaluation Functions.- 6. Projecting Ahead.- 7. Humans versus Computers.- 8. Overview.- 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 MachineExpertise.- 7. The Rule-Based Approach-mycin, prospector, and xcon.- 8. The Blackboard Approach (hearsay).- 9. The Set-Covering Approach (Frame Abduction).- 10. Multiple Paradigm Approaches.- 11. Expert System Shells.- 12. Recent Developments.- 13. Conclusions.- 14. References.- 7 Human Expertise.- 1. Introduction.- 2. The Theoretical Framework: Information-Processing Theory of Problem Solving.- 3. The Construction of a Problem Representation.- 4. The Role of Schemata in Problem Solving.- 5. Problem-Solving Strategies.- 6. The Development of Expertise.- 7. Conclusion.- 8. References.- 8 Machine Inference.- 1. Input of Knowledge.- 2. Machine Inference Based on Logic.- 3. The Production-Rule-Based Approach to Inference.- 4. The Frame-Based Approach to Inference.- 5. The Current Status of Machine Inference.- 6. References.- 9 Human Inference.- 1. Introduction.- 2. The Mental Logic Approach.- 3. The Mental Models Approach.- 4. The Nature of Inference.- 5. References.- 10 Machine Learning.- 1. Introduction.- 2. Learning Concepts from Examples: Problem Statement.- 3. Learning Concepts by Induction: A Detailed Example.- 4. Learning Decision Trees and Coping with Noise.- 5. Other Approaches to Learning and Bibliographical Remarks.- 6. References.- 11 Human Learning.- 1. Introduction.- 2. Schemata, Scripts, and Frames.- 3. Amnesia.- 4. Retrieval from Long-Term Memory.- 5. Concept Learning.- 6. Conclusions.- 7. References.- 12 Problem Solving by Human-Machine Interaction.- 1. Problem Solving for the Real World.- 2. Problem Solving Reconsidered from a Human Factors Perspective.- 3. Stages of the Problem-Solving Process.- 4. Human-Computer Problem Solving: Cases.- 5. A Retrospective Example.- 6. Summary and Conclusions.- 7. References.- 8. Further Reading.- 13 Human and MachineProblem Solving: A Comparative Overview.- 1. Introduction.- 2. Nonadversary Problems.- 3. Adversary Problems.- 4. Expertise.- 5. Inference.- 6. Learning.- 7. Solving Problems by Human-Computer Interaction.- 8. Concluding Comments.- 9. References.- Author Index.
1 Human and Machine Problem Solving: Toward a Comparative Cognitive Science.- 1. Introduction.- 2. Problem Solving.- 3. Perspectives.- 4. Some Issues.- 5. References.- 2 Nonadversary Problem Solving by Machine.- 1. Introduction.- 2. State Space Representation.- 3. Problem Reduction Representation: And/or Graphs.- 4. Planning.- 5. Conclusions.- 6. References.- 3 Human Nonadversary Problem Solving.- 1. Introduction.- 2. Constraints on a Model of Human Nonadversary 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.- 2. Dealing with the Adversary.- 3. Characteristics of the Search Process.- 4. Plans and Knowledge.- 5. Evaluation Functions.- 6. Projecting Ahead.- 7. Humans versus Computers.- 8. Overview.- 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 MachineExpertise.- 7. The Rule-Based Approach-mycin, prospector, and xcon.- 8. The Blackboard Approach (hearsay).- 9. The Set-Covering Approach (Frame Abduction).- 10. Multiple Paradigm Approaches.- 11. Expert System Shells.- 12. Recent Developments.- 13. Conclusions.- 14. References.- 7 Human Expertise.- 1. Introduction.- 2. The Theoretical Framework: Information-Processing Theory of Problem Solving.- 3. The Construction of a Problem Representation.- 4. The Role of Schemata in Problem Solving.- 5. Problem-Solving Strategies.- 6. The Development of Expertise.- 7. Conclusion.- 8. References.- 8 Machine Inference.- 1. Input of Knowledge.- 2. Machine Inference Based on Logic.- 3. The Production-Rule-Based Approach to Inference.- 4. The Frame-Based Approach to Inference.- 5. The Current Status of Machine Inference.- 6. References.- 9 Human Inference.- 1. Introduction.- 2. The Mental Logic Approach.- 3. The Mental Models Approach.- 4. The Nature of Inference.- 5. References.- 10 Machine Learning.- 1. Introduction.- 2. Learning Concepts from Examples: Problem Statement.- 3. Learning Concepts by Induction: A Detailed Example.- 4. Learning Decision Trees and Coping with Noise.- 5. Other Approaches to Learning and Bibliographical Remarks.- 6. References.- 11 Human Learning.- 1. Introduction.- 2. Schemata, Scripts, and Frames.- 3. Amnesia.- 4. Retrieval from Long-Term Memory.- 5. Concept Learning.- 6. Conclusions.- 7. References.- 12 Problem Solving by Human-Machine Interaction.- 1. Problem Solving for the Real World.- 2. Problem Solving Reconsidered from a Human Factors Perspective.- 3. Stages of the Problem-Solving Process.- 4. Human-Computer Problem Solving: Cases.- 5. A Retrospective Example.- 6. Summary and Conclusions.- 7. References.- 8. Further Reading.- 13 Human and MachineProblem Solving: A Comparative Overview.- 1. Introduction.- 2. Nonadversary Problems.- 3. Adversary Problems.- 4. Expertise.- 5. Inference.- 6. Learning.- 7. Solving Problems by Human-Computer Interaction.- 8. Concluding Comments.- 9. References.- Author Index.
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