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  Browse All Reviews > Computing Methodologies (I) > Artificial Intelligence (I.2) > Deduction And Theorem Proving (I.2.3) > Uncertainty, “Fuzzy,” And Probabilistic Reasoning (I.2.3...)  
  1-10 of 270 Reviews about "Uncertainty, “Fuzzy,” And Probabilistic Reasoning (I.2.3...)": Date Reviewed
  Link prediction in fuzzy social networks using distributed learning automata
Moradabadi B., Meybodi M.  Applied Intelligence 47(3): 837-849, 2017. Type: Article

Link prediction is an active area within the broader research on social network analysis (SNA) that tries to predict future links using a social network structure. The purpose of link prediction is to use the current structure of the social networ...

Nov 28 2017
  Reasoning about uncertainty (2nd ed.)
Halpern J.,  The MIT Press, Cambridge, MA, 2017. 504 pp. Type: Book (978-0-262533-80-5)

Halpern’s first edition, published in 2003, was recognized as: “a fine book and a mighty piece of scholarship” [1]; “an inspiring book, multifaceted and full of fresh reflections, findings and examples” [2]; and ̶...

Oct 25 2017
   A case-based reasoning system based on weighted heterogeneous value distance metric for breast cancer diagnosis
Gu D., Liang C., Zhao H.  Artificial Intelligence in Medicine 77 31-47, 2017. Type: Article

Case-based methods have previously been used to assist in treating cancers, and this paper introduces a number of significant modifications in the case of breast cancer. Specifically, these modifications relate to the way in which the distance bet...

Aug 3 2017
  Uncertainty and reduction of variable precision multigranulation fuzzy rough sets based on three-way decisions
Feng T., Fan H., Mi J.  International Journal of Approximate Reasoning 85(C): 36-58, 2017. Type: Article

To formalize decision making in information systems with incomplete data, object attributes may be represented as fuzzy sets. Rough sets abstract such systems by two sets that represent the lower and the upper approximation of which objects satisf...

Jul 27 2017
  Reasoning in non-probabilistic uncertainty: logic programming and neural-symbolic computing as examples
Besold T., Garcez A., Stenning K., van der Torre L., van Lambalgen M.  Minds and Machines 27(1): 37-77, 2017. Type: Article

Modeling human reasoning means dealing with uncertainty. The approach of conventional logic in which rules are absolute cannot be applied. Instead there is uncertainty: we need to use rules but accept that there may be cases where there are except...

Jun 28 2017
  Uncertain differential equations
Yao K.,  Springer International Publishing, New York, NY, 2016. 158 pp. Type: Book (978-3-662527-27-6)

Yao begins his book by stating that “uncertain differential equations” are a concept proposed by Liu in 2008. It appears, however, that what he calls uncertain differential equations are also known as stochastic differential equations,...

Jun 28 2017
  Models and methods for interval-valued cooperative games in economic management
Li D.,  Springer International Publishing, New York, NY, 2016. 137 pp. Type: Book (978-3-319289-96-0)

When R. E. Moore introduced the idea of interval computation in the later 1950s, his motivation was to automatically validate computational results of floating-point operations performed by digital computers. To accomplish the objective, the basic...

Jun 5 2017
  Recent contributions in intelligent systems
Sgurev V., Yager R., Kacprzyk J., Atanassov K.,  Springer International Publishing, New York, NY, 2016. 390 pp. Type: Book (978-3-319414-37-9)

This is a collection of the best papers from the 2012 IEEE Intelligent Systems Conference, which was held in Sofia, Bulgaria in September 2012. The average length of the papers in this volume is 20 pages. Since there are 20 papers in all, I will t...

May 25 2017
  Belief revision in structured probabilistic argumentation
Shakarian P., Simari G., Moores G., Paulo D., Parsons S., Falappa M., Aleali A.  Annals of Mathematics and Artificial Intelligence 78(3-4): 259-301, 2016. Type: Article

Traditional logic assumes, sometimes implicitly, that we are absolutely sure about each statement S in the knowledge base (KB); the question is what we can deduce from this knowledge. In practice, we often have some uncertainty ...

May 11 2017
  Bayesian methods in the search for MH370
Davey S., Gordon N., Holland I., Rutten M., Williams J.,  Springer International Publishing, New York, NY, 2016. 114 pp. Type: Book

One can seldom consider mathematics-heavy books as exciting page-turners. Yet, Bayesian methods in the search for MH370 is so far the best one I have encountered that could stand up to such a description. Published by Springer in its “...

Apr 21 2017
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