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A Systematic Review of Safety-Driven Approaches in Human-Robot Collaborative Systems. [PDF]

Sensors (Basel)
Khan A, Akhtar M, Qureshi SM, Mustafa M, Alsaleh NA, Ahmad I.   +5 more
europepmc   +1 more source

Clinician in the loop: a flawed solution for AI oversight.

BMJ
Toro-Tobon D, Ponte OP, Montori VM, Brito JP.   +3 more
europepmc   +1 more source

Amortized Inference in Probabilistic Reasoning.

, 2014
Gershman, Samuel, Goodman, Noah
openaire   +1 more source

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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
openaire   +2 more sources

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
openaire   +1 more source

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 ...
openaire   +1 more source

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.
openaire   +1 more source

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
openaire   +2 more sources