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In this book Simon Parsons describes qualitative methods for reasoning under uncertainty, "uncertainty" being a catch-all term for various types of imperfect information. The advantage of qualitative methods is that they do not require precise numerical information. Instead, they work with abstractions such as interval values and information about how values change. The author does not invent completely new methods for reasoning under uncertainty but provides the means to create qualitative versions of existing methods. To illustrate this, he develops qualitative versions of probability theory, possibility theory, and the Dempster-Shafer theory of evidence. According to Parsons, these theories are best considered complementary rather than exclusive. Thus the book supports the contention that rather than search for the one best method to handle all imperfect information, one should use whichever method best fits the problem. This approach leads naturally to the use of several different methods in the solution of a single problem and to the complexity of integrating the results--a problem to which qualitative methods provide a solution.
About the Author
Simon Parsons is a Associate Professor in the Department of Computer and Information Science at Brooklyn College and the editor of the journal Knowledge Engineering Review.
In this book Simon Parsons describes qualitative methods for reasoning under uncertainty, "uncertainty" being a catch-all term for various types of imperfect information. The advantage of qualitative methods is that they do not require precise numerical information. Instead, they work with abstractions such as interval values and information about how values change. The author does not invent completely new methods for reasoning under uncertainty but provides the means to create qualitative versions of existing methods. To illustrate this, he develops qualitative versions of probability theory, possibility theory, and the Dempster-Shafer theory of evidence. According to Parsons, these theories are best considered complementary rather than exclusive. Thus the book supports the contention that rather than search for the one best method to handle all imperfect information, one should use whichever method best fits the problem. This approach leads naturally to the use of several different methods in the solution of a single problem and to the complexity of integrating the results--a problem to which qualitative methods provide a solution.
About the Author
Simon Parsons is a Associate Professor in the Department of Computer and Information Science at Brooklyn College and the editor of the journal Knowledge Engineering Review.
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Inside This Book
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First Sentence:
The discipline of artificial intelligence has as its aim the construction of computer-based systems that exhibit intelligence. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
intercausal reasoning, using qualitative algebras, monotonic influence diagrams, loose knee bodies, uncertainty handling formalisms, qualitative probabilistic networks, qualitative derivatives, separable derivatives, local computation mechanism, using evidence theory, combinator tables, probabilistic causal networks, handling incompleteness, local computation scheme, certainty networks, protein topology prediction, handling imperfect information, qualitative multiplication, pignistic transformation, minimum specificity, incoming sign, singly connected network, using belief functions, quantitative formalisms, basic belief mass Key Phrases - Capitalized Phrases (CAPs): (learn more)
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The discipline of artificial intelligence has as its aim the construction of computer-based systems that exhibit intelligence. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
intercausal reasoning, using qualitative algebras, monotonic influence diagrams, loose knee bodies, uncertainty handling formalisms, qualitative probabilistic networks, qualitative derivatives, separable derivatives, local computation mechanism, using evidence theory, combinator tables, probabilistic causal networks, handling incompleteness, local computation scheme, certainty networks, protein topology prediction, handling imperfect information, qualitative multiplication, pignistic transformation, minimum specificity, incoming sign, singly connected network, using belief functions, quantitative formalisms, basic belief mass Key Phrases - Capitalized Phrases (CAPs): (learn more)
Jack Dulouz, Constraint Added Change New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Front Flap | Table of Contents | First Pages | Index | Back Flap | Back Cover | Surprise Me!
Citations (learn more)
This book cites 83
books:
See all 83 books this book cites
1
book
cites this book:
- Uncertainty in Artificial Intelligence (Machine Intelligence and Pattern Recognition (Volume 4) (Vol 1) by R. K. Ghatnagar on 28 pages
- Subjective Probability by George Wright on 6 pages
- Judgment and Decision Making: An Interdisciplinary Reader (Cambridge Series on Judgment and Decision Making) by Terry Connolly on 4 pages
- Non-Standard Logics for Automated Reasoning by P. Smets on 4 pages
- Artificial Intelligence by Nils J. Nilsson on 4 pages
See all 83 books this book cites
- Evaluation of the Built Environment for Sustainability by Peter Brandon on page 565
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