Knowledge-Based Simulation

1. Front Matter

   Pages i-xiv

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2. Methodology

   1. Front Matter

      Pages ins1-ins1

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   2. Application of Artificial Intelligence Techniques to Simulation

      • Norman R. Nielsen

      Pages 1-19

   3. The DEVS-Scheme Modelling and Simulation Environment

      • Tag Gon Kim, Bernard P. Zeigler

      Pages 20-35

   4. Methods for Qualitative Modeling in Simulation

      • Paul A. Fishwick

      Pages 36-52

   5. Dynamic Templates and Semantic Rules for Simulation Advisors and Certifiers

      • Tuncer I. Ören

      Pages 53-76

   6. Knowledge Acquisition Based on Representation (KAR) for Design Model Development

      • Jhyfang Hu, Jerzy W. Rozenblit

      Pages 77-94

   7. Automatic Model Generation for Troubleshooting

      • Arie Ben-David

      Pages 95-107

   8. From CAD/CAM to Simulation: Automatic Model Generation for Mechanical Devices

      • Andrew Gelsey

      Pages 108-132

3. Application

   1. Front Matter

      Pages ins3-ins3

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   2. Knowledge-Based Simulation at the RAND Corporation

      • Jeff Rothenberg

      Pages 133-161

   3. An Architecture for High-Level Human Task Animation Control

      • Jeffrey Esakov, Norman I. Badler

      Pages 162-199

   4. The Acquisition of Cognitive Simulation Models: A Knowledge-Based Training Approach

      • Kent E. Williams, Richard E. Reynolds

      Pages 200-222

   5. Strategic Automatic Discovery System (STRADS)

      • Colleen M. Oresky, Douglas B. Lenat, Albert Clarkson, Stephen H. Kaisler

      Pages 223-260

   6. Uncertainty Management in Battle-Planning Software

      • Ben P. Wise, Richard B. Modjeski

      Pages 261-276

4. Back Matter

   Pages 277-293

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Knowledge-Based Simulation: Methodology and Application represents a recent compilation of research material that reviews fundamental concepts of simulation methodology and knowledge-based simulation applications. Knowledge-based simulation represents a new and exciting bridge area linking the fields of computer simulation and artificial intelligence. This book will appeal to both theorists and practitioners who require simulation to solve complex problems. A primary attraction of the book is its emphasis on both methodology and applications. In this way, the reader can explore new methods for encoding knowledge-inten- sive information into a simulation model, and new applications that utilize these methods.

• Animation
• Rechnerunterstütze Konstruktion
• architecture
• artificial intelligence
• bridge
• computer simulation
• computer-aided design (CAD)
• computer-aided manufacturing (CAM)
• control
• intelligence
• knowledge
• model
• modeling
• simulation
• uncertainty

• Department of Computer and Information Science, University of Florida, Gainesville, USA

  Paul A. Fishwick

• U.S. Army Operational Test and Evaluation Agency, Alexandria, USA

  Richard B. Modjeski

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