- Hardcover: 416 pages
- Publisher: Prentice Hall; 1 edition (June 12, 2000)
- Language: English
- ISBN-10: 0130951927
- ISBN-13: 978-0130951922
- Product Dimensions: 7.2 x 1.2 x 9.2 inches
- Shipping Weight: 2.3 pounds
- Average Customer Review: Be the first to review this item
- Amazon Best Sellers Rank: #12,761,579 in Books (See Top 100 in Books)
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Planning for Real Time Event Response Management Hardcover – June 12, 2000
From the Back Cover
* Real-time intelligent agents: solutions and applications
* Choosing and integrating the best planning techniques
* Reaction decision frameworks and language frameworks
* Includes a complete case study
The act of planning for and responding to real-time events has been an area of intense investigation in the past few years. Planning for Real Time Event Response Management offers this state-of-the-art technology in depth and demonstrates how it can be employed for maximum effect. The techniques presented here are applicable to an exceptionally broad range of problems, from web-based shopping assistants to robots, medicine to financial analysis. Coverage includes:
* Conventional and reactive planning: when to use each, and when to avoid planning
* A flight planning example used throughout the book to bring together the concepts
* Key reactive planning techniques such as Brooks' subsumption architecture and Rosenschein/Kaelbling's situated automataand their limitations
* Contingencies: building intelligent agents that can handle unanticipated events
* Applying planning paradigms to real-world domains
* Building Reaction Decision Frameworks that integrate conventional and reactive planning
* Solving problems at execution time: anytime algorithms and real-time architectures
* Real-time planning: integrating artificial intelligence and real-time computing
Planning for Real Time Event Response Management introduces a framework for describing reactive behavior that can be used in many knowledge domains, concluding with a preview of the next steps to be taken in intelligent agent development, including integration of real-time problem solving languages with existing Internet infrastructures.
About the Author
Dr. David Ash graduated from Stanford University in 1993 with his Ph.D. in artificial intelligence. After graduating, Dr. Ash worked until 1996 as a software developer, quantitative analyst, and trader at D.E. Shaw & Co., a Manhattan financial services firm. Since 1996, Dr. Ash has been employed as an artificial intelligence consultant with MindBox, Inc., providing consulting services to MindBox clients at various locations on the East Coast.
Dr. Vlad Dabija holds an engineering degree in computers from the Polytechnic Institute in Bucuresti, Romania, a Ph.D. degree in artificial intelligence from Stanford University, and an MBA with Distinction from the Wharton School of the University of Pennsylvania. He worked in several computer technology, production, research, and academic institutions in the US, Japan, and Western and Eastern Europe before joining the venture capital firm CMEA Ventures.
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