| Grant number: | 11/19860-4 |
| Support Opportunities: | Scholarships abroad - Research |
| Start date: | August 01, 2012 |
| End date: | July 31, 2013 |
| Field of knowledge: | Physical Sciences and Mathematics - Computer Science |
| Principal Investigator: | Marcelo Finger |
| Grantee: | Marcelo Finger |
| Host Investigator: | Bart Selman |
| Host Institution: | Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo , SP, Brazil |
| Institution abroad: | Cornell University, United States |
| Associated research grant: | 08/03995-5 - Logprob: probabilistic logic --- foundations and computational applications, AP.TEM |
Abstract Deductive reasoning and probabilistic reasoning have experienced an enormous development within the Artificial Intelligence community in the past decades. While deductive reasoning has been part of AI since its birth, the computational complexity of its problems ranges between the intractable and the undecidable. The realization that not all instances of intractable problems are hard prompted the development of efficient SAT solvers and applications and the size of problems dealt by this approach significantly started to increase, and now can solve instances with hundreds of thousands of variables. Probabilities have also been part of AI since its beginnings, but probabilistic reasoning in AI has had its greatest development starting with the consolidation of its computational properties, which lead to further developments in reasoning about causalities and the large employment of graph-based probabilistic models in a wide range of applications. The competition and cross-fertilization between those two approaches have promoted and accelerated the development of the field of AI itself. In this project, we are interested in a combined form of deductive-probabilistic reasoning. We are interested in a particular form of interaction between these two forms of reasoning called probabilistic logic, and the associated problem of probabilistic satisfiability (PSAT) and logic-probabilistic inference and its potential to practical AI applications. (AU) | |
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