Contributions to a Computer-Based Theory of Strategies, Softcover reprint of the original 1st ed. 1990

People use the word strategy in a variety of different contexts. The term has connotations ranging from statesmanship to economic planning, and has become pervasive in the social sciences. We also talk about "problem solving strategies" and "corporate strategy" in a large business enterprise. The concept of strategy applies whenever a sequence of goal-oriented actions is based on large-scale and long-range planning. This monograph gives a systematic overview of the theory of strategies, a new area of enquiry developed over the past two decades by the author and his team. The projects described have clearly defined research objectives and are based on realistic assumptions about the environments in which the programming systems will work, and about the constraints and requirements they have to satisfy. Applications of the systems range over various aspects of air traffic control, automatic verification and validation of discrete-event simulation models, econometric model building, distributed planning systems for manufacturing, control of traffic lights, and others. The book is aimed at researchers, teachers and students in computer science, management science and certain areas of engineering. The reader should have some maturity in computer science and mathematics, and familiarity with the basic concepts of artificial intelligence.

1. Introduction.- 2. Basic Concepts of Strategies, Decision Making, and Planning.- 3. The Quasi-Optimizer (QO) System.- 3.1. Introduction and Research Objectives.- 3.2. A Brief Survey of Related Work.- 3.3. The Approach.- 3.4. System Modules.- 3.4.1. The QO-1 Module.- 3.4.2. The QO-2 Module.- 3.4.3. The QO-3 Module.- 3.4.4. The QO-4 Module.- 3.4.5. The QO-5 Module.- 3.4.6. The QO-6 Module.- 3.5. The Implementation.- 3.6. Applications.- 3.7. Summary.- 3.8. Acknowledgements.- 4. The Advice Taker/Inquirer (AT/I).- 4.1. Introduction and Research Objectives.- 4.2. The Approach.- 4.2.1. The Learning Phase.- 4.2.2. The Operational Phase.- 4.3. The Implementation.- 4.3.1. The Definition Facility.- 4.3.2. Prototypes.- 4.3.3. Entity Instantiation.- 4.3.4. Production Rules.- 4.3.5. Procedures and Functions.- 4.3.6. The Units of Measurement.- 4.3.7. The Agenda Mechanism.- 4.3.8. The Inference Engine.- 4.3.9. The Current Situation Assessor.- 4.3.10. The Hypothesizer.- 4.3.11. The Historian.- 4.4. Benefits of Using the AT/I.- 4.5. Applications in Assembly Line Balancing and Street Traffic Light Control.- 4.5.1. The Definition of the Assembly Line Balancing Problem.- 4.5.2. The Definition of the Street Traffic Light Control Problem.- 4.6. Summary.- 4.7. Acknowledgements.- 5. The Generalized Production Rule System (GPRS).- 5.1. Introduction and Research Objectives.- 5.2. The Approach.- 5.2.1. Morphs and the Morph-Fitting Program.- 5.2.2. The Knowledge Base.- 5.2.3. The Optimization of the Knowledge Base.- 5.2.4. The Estimation of Values of Hidden Variables.- 5.3. System Modules.- 5.3.1. The GPRS-1 Module.- 5.3.2. The GPRS-2 Module.- 5.3.3. The GPRS-3 Module.- 5.3.4. The GPRS-4 Module.- 5.3.5. The GPRS-5 Module.- 5.3.6. The GPRS-6 Module.- 5.3.7. The Utility Programs.- 5.4. The Implementation.- 5.4.1. A Pattern Search Method for Optimization.- 5.4.2. The Interaction with the System.- 5.5. Applications.- 5.6. Summary.- 5.7. Acknowledgements.- 6. Distributed Planning and Problem Solving Systems (DPPSS).- 6.1. Introduction and Research Objectives.- 6.1.1. Traditional Al Approaches to Centralized Planning.- 6.1.2. On Distributed Planning and Problem Solving.- 6.1.3. General Categories of Application of DPPSS.- 6.1.4. The Four Phases of Network Activity within DPPSS.- 6.1.5. A Few Major Issues with DPPSS.- 6.1.6. Other Dimensions of Classification of Distributed Planning.- 6.1.7. Global Coherence with Decentralized Control.- 6.1.8. The Issue of Time-Criticality.- 6.1.9. Two Successful Approaches to DPPSS.- 6.2. A Distributed System for Air Traffic Control.- 6.2.1. General Issues.- 6.2.2. Our Approach.- 6.2.3. On Coordinator Selection.- 6.2.4. Configuration Diagrams and Tables.- 6.2.5. Connection through Mutual Selection.- 6.2.6. Distributed Scratch Pads and the ‘Self-Heal’ Process.- 6.2.7. Definitions of Incidents, Conflicts, Violations and Space Measures.- 6.2.8. The Kernel Design of the Individual Processors.- 6.2.9. The Mechanism to Ensure Navigational Safety.- 6.2.10. The Priority-Factor and Its Use.- 6.2.11. The Incremental Shallow Planning.- 6.2.12. The Coordinator-Coworker Structure as the Organizational Scheme.- 6.2.13. The Process of Resolving Incidents.- 6.2.14. The Three Organizational Structures to Be Compared.- 6.2.15. The Distributed Air Traffic Control Testbed.- 6.2.16. The Results of Empirical Investigations.- 6.2.17. Conclusions.- 6.2.18. Future Research Directions.- 6.3. A Distributed System for Manufacturing Control.- 6.3.1. Introduction.- 6.3.2. The General Paradigm.- 6.3.3. The Approach.- 6.4. A System for Distributed and Moving Resources and Tasks.- 6.4.1. Introduction.- 6.4.2. A Domain of Application.- 6.4.3. The Approach.- 6.4.4. The Activities at Different Levels.- 6.4.5. The Salient Features of the System.- 6.5. A Distributed System for Street Traffic Light Control.- 6.5.1. Introduction.- 6.5.2. The Approach.- 6.5.3. The Control Strategies.- 6.5.4. The Information Communicated between Controllers.- 6.5.5. Some Notation and Definitions to Be Used with the Rules.- 6.5.6. The Rules to Control the Traffic Light Regime.- 6.5.7. On Scenario Generation.- 6.5.8. The Optimization of the Rule Base.- 6.6. Summary.- 6.7. Acknowledgements.- 7. Causal Modelling Systems (CMS and NEXUS).- 7.1. Introduction and Perspectives on Causation.- 7.1.1. The Philosophical View of Causality.- 7.1.2. The Probabilistic, Statistical and Logical Approaches to Causality.- 7.1.3. The Sociological View of Causality.- 7.1.4. The Psychological View of Causality.- 7.1.5. Causality in Linguistics and in Natural Language Understanding Systems.- 7.1.6. Causality in Artificial Intelligence.- 7.2. The Causal Modelling System CMS.- 7.2.1. Research Objectives.- 7.2.2. The Development of the Causal Model.- 7.2.3. The Operation of CMS.- 7.2.4. Issues of Implementation.- 7.2.5. Applications of CMS.- 7.3. The Causal Modelling System NEXUS.- 7.3.1. Introduction and Research Objectives.- 7.3.2. The Causal Reasoning and Learning Schemas.- 7.3.3. On Knowledge Representation.- 7.3.4. Reasoning in NEXUS.- 7.3.5. Causal Learning in NEXUS.- 7.3.6. Issues of Implementation.- 7.3.7. Areas of Applications.- 7.4. Summary.- 7.5. Acknowledgements.- 8. The Predictive Man-Machine Environment (PMME).- 8.1. Introduction and Research Objectives.- 8.1.1. On Decision Support Systems.- 8.1.2. The Predictive Man-Machine System.- 8.2. The Simulated Air Traffic Control Environment.- 8.2.1. Some Terminology.- 8.2.2. Attempts at the Implementation.- 8.2.3. The Operation.- 8.3. An Evaluation of the Predictive Man-Machine Environment.- 8.3.1. The Experimental Set-Up.- 8.3.2. The Results of Experiments.- 8.4. Summary.- 8.5. Acknowledgements.- 9. Overall Summary.- References.