Results 241 to 250 of about 794,201 (275)

Probabilistic reasoning by neurons

Nature, 2007
Our brains allow us to reason about alternatives and to make choices that are likely to pay off. Often there is no one correct answer, but instead one that is favoured simply because it is more likely to lead to reward. A variety of probabilistic classification tasks probe the covert strategies that humans use to decide among alternatives based on ...
Tianming, Yang, Michael N, Shadlen
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A probabilistic commonsense reasoner

International Journal of Intelligent Systems, 1990
Summary: We claim that probability is epistomologically adequate and describe a reasoning formalism based on the probability calculus and conditional independence that requires only a knowledge base of probabilistic inequalities. Numerical distributions are not required, but we depart in a significant way from the ``logicist'' school of AI: rather than
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Information and probabilistic reasoning

Annals of Mathematics and Artificial Intelligence, 1990
In this paper, the relationship between information and reasoning is investigated and a parallel reasoning method is proposed based on information theory, in particular the principle of minimum cross entropy. Some technical issues, such as multiple uncertain evidence, complicated constraints, small directed cycles and decomposition of underlying ...
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Probabilistic reasoning and probabilistic neural networks

International Journal of Intelligent Systems, 1992
Summary: The Boltzmann machine is a probabilistic neural network describing the associative dependency of variables. It yields a probability distribution, which is a special case of the distribution generated by probabilistic inference networks.
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BUNDLE: A Reasoner for Probabilistic Ontologies

2013
Representing uncertain information is very important for modeling real world domains. Recently, the DISPONTE semantics has been proposed for probabilistic description logics. In DISPONTE, the axioms of a knowledge base can be annotated with a set of variables and a real number between 0 and 1. This real number represents the probability of each version
RIGUZZI, Fabrizio, LAMMA, Evelina, BELLODI, Elena, ZESE, Riccardo   +3 more
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Probabilistic quantitative temporal reasoning

Proceedings of the Symposium on Applied Computing, 2017
Temporal reasoning, in the form of propagation of temporal constraints, is an important topic in Artificial Intelligence. The current literature in the area is moving from the treatment of "crisp" temporal constraints to fuzzy or probabilistic constraints, to account for different forms of uncertainty and\or preferences.
Terenziani P., Andolina A.
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Probabilistic default reasoning

2005
We present an algorithm that is able to integrate uncertain probability statements of different default levels. In case of conflict between statements of different levels the statements of the lower levels are ignored. The approach is applicable to inference networks of arbitrary structure including loops and cycles.
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Signal classification by probabilistic reasoning

2013 IEEE Radio and Wireless Symposium, 2013
Much of the work into developing environmental and network awareness for cognitive radios has been focused on developing new metrics to identify the modulation schemes in use by neighboring radio nodes. Unfortunately, the metrics are used to derive only hard decisions which are often threshold-based and therefore unable to assign a measure of ...
Christopher Ian Phelps, R. Michael Buehrer   +1 more
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Probabilistic Reasoning by SAT Solvers

2009
In a series of papers we have shown that fundamental probabilistic reasoning problems can be encoded as hybrid probabilistic logic programs with probabilistic answer set semantics described in [24]. These probabilistic reasoning problems include, but not limited to, probabilistic planning [28], probabilistic planning with imperfect sensing actions [29],
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Probabilistic reasoning in the law

Science and Justice - Journal of the Forensic Science Society, 1998
C G G Aitken
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