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Qualitative Reasoning: Modeling and Simulation with Incomplete Knowledge (Artificial Intelligence) download epub

by Benjamin Kuipers


Epub Book: 1430 kb. | Fb2 Book: 1974 kb.

Qualitative reasoning is one of the most vigorous areas in artificial intelligence

Qualitative reasoning is one of the most vigorous areas in artificial intelligence The primary tool for this work is the author's QSIM algorithm, which is discussed in detail. Qualitative models are better able than traditional models to express states of incomplete knowledge about continuous mechanisms. Qualitative simulation guarantees to find all possible behaviours consistent with the knowledge in the model. This expressive power and coverage is important in problem solving for diagnosis, design, monitoring and explanation.

Qualitative simulation guarantees to find all possible behaviors consistent with the knowledge in the model. This expressive power and coverage is important in problem solving for diagnosis, design, monitoring, explanation, and other applications of artificial intelligence. Scientists and engineers who have had a solid introduction to AI, however, will be able to use this book for self-instruction in qualitative modeling and simulation methods. Artificial Intelligence series.

A body of methods that have been developed for building and simulating qualitative models of physical systems where knowledge of that system is incomplete.

oceedings{veR, title {Qualitative reasoning - modeling and simulation with incomplete . Abstract Recently developed methods for qualitative reasoning may fill an important gap in the modeling and control toolkit.

oceedings{veR, title {Qualitative reasoning - modeling and simulation with incomplete knowledge}, author {Benjamin Kuipers}, booktitle {Automatica}, year {1989} }. Benjamin Kuipers. Published in Automatica 1989.

Qualitative Reasoning: Modeling and Simulation with Incomplete Knowledge. Advances in Qualitative and Semi-Quantitative Simulation. Cambridge, MA: MIT Press. Clancy's dissertation provides abstraction, n, and focusing methods reduce the problem of intractable branching to the intrinsic complexity of the model, not limitations of the simulation method. Berleant's work (based on his 1991 dissertation) presents Q3, and Kay's dissertation presents NSIM and SQUID, which do semi-quantitative reasoning on the foundation provided by the qualitative behavior. Herbert Kay, Bernhard Rinner and Benjamin Kuipers.

Qualitative reasoning: modelling and simulation with incomplete information by Benjamin Kuipers, MIT Press, Cambridge, MA, pp 418, ISBN 0-262-11190-X, 1994.

Qualitative simulation is a key inference process in qualitative causal reasoning. However, the precise meaning of the different proposals and their relation with differential equations is often unclear. In this paper, we present a precise definition of qualitative structure and behavior descriptions as abstractions of differential equations and continuously differentiable functions. We present a new algorithm for qualitative simulation that generalizes the best features of existing algorithms, and allows direct comparisons among alternate approaches.

Kuipers B. Qualitative Reasoning: Modelling and Simulation with Incomplete Knowledge, Automatica, Vol. 25. No 4, pp. 571–585. 14. Lee, W. Kuipers B. Non-intersection of trajectories in qualitative phase space: A global constraint for qualitative simulation, Proc.

B. Kuipers, Qualitative Reasoning: Modeling and Simulation with Incomplete Knowledge.

He is best known for his work on computational models of cognitive maps, robot exploration and mapping methods, the qualitative simulation algorithm QSIM, and foundational learning methods. Family origins, education, and academic career. B.

It is currently an unsolved problem in Artificial General Intelligence and is a focus of the Paul Allen Institute for Artificial Intelligence. The first AI program to address common sense knowledge was Advice Taker in 1959 by John McCarthy.

This book presents, within a conceptually unified theoretical framework, a body of methods that have been developed over the past fifteen years for building and simulating qualitative models of physical systems—bathtubs, tea kettles, automobiles, the physiology of the body, chemical processing plants, control systems, electrical systems—where knowledge of that system is incomplete. The primary tool for this work is the author's QSIM algorithm, which is discussed in detail. Qualitative models are better able than traditional models to express states of incomplete knowledge about continuous mechanisms. Qualitative simulation guarantees to find all possible behaviors consistent with the knowledge in the model. This expressive power and coverage is important in problem solving for diagnosis, design, monitoring, explanation, and other applications of artificial intelligence. The framework is built around the QSIM algorithm for qualitative simulation and the QSIM representation for qualitative differential equations, both of which are carefully grounded in continuous mathematics. Qualitative simulation draws on a wide range of mathematical methods to keep a complete set of predictions tractable, including the use of partial quantitative information. Compositional modeling and component-connection methods for building qualitative models are also discussed in detail. Qualitative Reasoning is primarily intended for advanced students and researchers in AI or its applications. Scientists and engineers who have had a solid introduction to AI, however, will be able to use this book for self-instruction in qualitative modeling and simulation methods. Artificial Intelligence series
Qualitative Reasoning: Modeling and Simulation with Incomplete Knowledge (Artificial Intelligence) download epub
Computer Science
Author: Benjamin Kuipers
ISBN: 026211190X
Category: Computers & Technology
Subcategory: Computer Science
Language: English
Publisher: The MIT Press; First Edition edition (August 29, 1994)
Pages: 452 pages