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  Browse All Reviews > Computing Methodologies (I) > Artificial Intelligence (I.2) > Deduction And Theorem Proving (I.2.3)  
 
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  1-10 of 325 Reviews about "Deduction And Theorem Proving (I.2.3)": Date Reviewed
   How testing helps to diagnose proof failures
Petiot G., Kosmatov N., Botella B., Giorgetti A., Julliand J. Formal Aspects of Computing 30(6): 629-657, 2018.  Type: Article

Petiot et al. present testing software components that help optimize the effort of “applying deductive verification to formally prove that a [computer] program respects its formal specification.”...

May 10 2019
   On the logos: a naïve view on ordinary reasoning and fuzzy logic
Trillas E., Springer International Publishing, New York, NY, 2017. 213 pp.  Type: Book (978-3-319560-52-6)

There are many books that you read to get answers to questions and doubts, and there are few books that leave you with more questions and doubts than before reading them. Yet, you can still be satisfied with such books because they giv...

Apr 9 2018
  A progression semantics for first-order logic programs
Zhou Y., Zhang Y. Artificial Intelligence 250 58-79, 2017.  Type: Article

This paper looks at the relationship between classical logic and practical systems, which employ logic-based techniques to process data. The emphasis is on answer set programming (ASP) and Datalog rather than Prolog. While Prolog can b...

Mar 9 2018
  A new perspective on nonmonotonic logics
Gabbay D., Schlechta K., Springer International Publishing, New York, NY, 2016. 365 pp.  Type: Book (978-3-319468-15-0)

Nonmonotonic reasoning derives plausible conclusions from a theory. The theory is sometimes called background knowledge. If new information becomes available showing that some conclusions are wrong, one has to retract them. A common ap...

Feb 7 2018
  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 ex...

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 ...

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 ob...

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 ther...

Jun 28 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 ...

May 11 2017
  The fundamentals of computational intelligence: system approach
Zgurovsky M., Zaychenko Y., Springer International Publishing, New York, NY, 2016. 373 pp.  Type: Book (978-3-319351-60-5)

Not for the faint-hearted or those without mathematical knowledge, the rhythm of this book is as follows: first, it provides a profound formal description grounded in mathematics in some sections of chapters, and then there is a sectio...

Apr 21 2017
 
 
 
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