Building Knowledge-Based Systems for Natural Resource Management - Rauscher, H. Michael

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Résumé :
This is the first book to provide a detailed process for planning, designing, implementing, and testing knowledge-based systems for natural resource management. It presents material on all these major aspects of building a deliverable system. Equipped with these techniques, managers and scientists will improve their ability to solve complex resource problems that are multidisciplinary in scope and for which mathematical approaches prove insufficient. Fully describing the various components of these systems, this important work includes discussions on system design, knowledge acquisition, prototyping, knowledge verification and validation, implementation, and system delivery. To further illuminate the material presented, it contains a tutorial on the knowledge-based programming environment PROLOG as well as many examples of expert system development, including one for forest management. emBuilding Knowledge-Based Systems/em emfor Natural Resource Management/em demonstrates how knowledge can be effectively organized and administered, enabling natural resource professionals to respond intelligently to natural resource problems. This book also provides researchers and students with an essential resource for understanding this useful technology.

Sommaire:
1 AI and Natural Resource Management.- 1.1 Natural Resources.- 1.2 Resource Management.- 1.3 Knowledge: A Historical Framework.- 1.4 Artificial Intelligence.- 1.5 Knowledge-Based Systems.- 1.6 Some Potential Benefits of Knowledge-Based Systems.- 1.7 Limitations of Knowledge-Based Systems.- 1.8 Suitable Application Areas.- 1.9 Summary.- 2 Knowledge-Based Systems: Representation and Search.- 2.1 An Overview of KBS Organization.- 2.2 The Knowledge Base and Knowledge Representation.- 2.3 Working Memory.- 2.4 Search: Inference and Control.- 2.5 Knowledge-Based System Architectures.- 2.6 Summary and Additional Readings.- 3 Other Knowledge System Components.- 3.1 Explanation and Justification.- 3.2 User Interface.- 3.3 Learning Capabilities.- 3.4 Interfacing to Conventional Programs and Data Files.- 3.5 Inexact Reasoning and Uncertainty.- 3.6 Summary and Additional Readings.- 4 Planning the Application.- 4.1 Parallelism and Cycling in the Development Process.- 4.2 Alternative Development Methods.- 4.3 Problem Definition.- 4.4 Summary.- 5 Knowledge Acquisition.- 5.1 Knowledge Acquisition in Overview.- 5.2 Surface Versus Deep Knowledge.- 5.3 Knowledge Acquisition Strategies.- 5.4 Creating a Domain Ontology.- 5.5 Acquisition Methods.- 5.6 Domain Ontology and Acquisition Methods.- 5.7 Knowledge Acquisition Scenarios.- 5.8 Knowledge Acquisition Guidelines.- 5.9 Expert Biases and Eliciting Uncertain Knowledge.- 5.10 Summary and Additional Readings.- 6 Designing the Application.- 6.1 Application Design = Knowledge Model + Human Factors Model.- 6.2 Pre-Implementation Topics.- 6.3 Prototyping.- 6.4 Summary.- 7 Programming Knowledge Systems in Prolog.- 7.1 Why Prolog?.- 7.2 Introduction to Prolog.- 7.3 Summary and Additional Readings.- 8 An Initial Prototype Using Native PROLOG.- 8.1 Knowledge-Based System Architecture.- 8.2 The Domain Level.- 8.3 The Tactical Control Level.- 8.4 System Control Level.- 8.5 Putting It All Together.- 8.6 From Native Prolog to Meta-PROLOG Programming.- 8.7 Summary.- 9 A PROLOG Toolkit Approach to Developing Forest Management Knowledge-Based Systems.- 9.1 A KBS Toolkit.- 9.2 DSSTOOLS System Architecture.- 9.3 Domain Level.- 9.4 Tactical Control Level.- 9.5 System Control Level.- 9.6 Summary.- 10 System Evaluation and Delivery.- 10.1 System Evaluation.- 10.2 Delivery.- 10.3 Summary.- Appendix A Roots of Artificial Intelligence.- A.1 A Definition of Artificial Intelligence.- A.2 Dreams of Intelligent Artifacts.- A.2.1 Ancient Dreams.- A.2.2 Modern Nightmares.- A.3 Realization.- A.3.1 Realize What?.- A.3.2 Mind as a Tool.- A.4 Knowledge.- A.4.1 Group Knowledge.- A.4.2 Hierarchical Structure.- A.4.3 Chunks.- A.4.4 Representation of Chunks.- A.4.4.1 Frames and Semantic Networks.- A.4.4.2 State Space.- A.4.4.3 Artificial Neural Networks.- A.4.4.4 Rules.- A.4.4.5 Threaded Chunks.- A.5 Goals.- A.6 Reaching the Goal.- A.6.1 Generate-and-Test.- A.6.2 Goal Reduction.- A.6.3 State-Space Search.- A.6.4 Genetic Algorithms and Emergent Behavior.- A.7 Interfacing, or Conversing with the Statue.- A.8 Testing and Conclusion.- Biliography.