Building Knowledge-Based Systems for Natural Resource Management
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Building Knowledge-Based Systems for Natural Resource Management

Building Knowledge-Based Systems for Natural Resource Management


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About the Book

During the last 15 years, the ability to capture and distribute human decision-making skills on a desktop computer has created new possibilities for the management of many types of resources. The resource may be monetary, social, natural or economic. Computer programs that function with a certain degree of skill in solving problems are referred to as expert systems or knowledge systems. Natural resource management appears to be particularly well suited to these types of decision aids. As a result, there is a growing demand for personnel trained in knowledge system development. This guide attempts to cover the major areas which a conservation biologist or research manager requires in order to apply knowledge systems: knowledge acquisition; building a prototype; moving to a final system; knowledge validation; and implementation. Of particular interest is a section discussing the features of a resource management problem appropriate, or inappropriate, for an expert system application.

Table of Contents:
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.


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Product Details
  • ISBN-13: 9780412019210
  • Publisher: Chapman and Hall
  • Publisher Imprint: Chapman and Hall
  • Height: 235 mm
  • No of Pages: 386
  • Width: 155 mm
  • ISBN-10: 0412019213
  • Publisher Date: 31 Mar 1996
  • Binding: Hardback
  • Language: English
  • Returnable: Y


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