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Related papers: Unknown Truths and Unknowable Truths

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Explainability and comprehensibility of AI are important requirements for intelligent systems deployed in real-world domains. Users want and frequently need to understand how decisions impacting them are made. Similarly it is important to…

Computers and Society · Computer Science 2019-07-10 Roman V. Yampolskiy

Plausible reasoning concerns situations whose inherent lack of precision is not quantified; that is, there are no degrees or levels of precision, and hence no use of numbers like probabilities. A hopefully comprehensive set of principles…

Artificial Intelligence · Computer Science 2017-04-05 David Billington

Automated reasoning about uncertain knowledge has many applications. One difficulty when developing such systems is the lack of a completely satisfactory integration of logic and probability. We address this problem directly. Expressive…

Logic in Computer Science · Computer Science 2012-09-13 Marcus Hutter , John W. Lloyd , Kee Siong Ng , William T. B. Uther

I think we can agree that dealing with uncertainty is not easy. Probability is the main tool for dealing with uncertainty, and we know there are many probability-related puzzles and paradoxes. Here I describe a rather idiosyncratic…

Other Statistics · Statistics 2022-01-19 Yudi Pawitan

Here, by introducing a version of "Unexpected hanging paradox" we try to open a new way and a new explanation for paradoxes, similar to liar paradox. Also, we will show that we have a semantic situation which no syntactical logical system…

Other Computer Science · Computer Science 2012-03-15 Farzad Didehvar

A simple framework for reasoning under uncertainty and intervention is introduced. This is achieved in three steps. First, logic is restated in set-theoretic terms to obtain a framework for reasoning under certainty. Second, this framework…

Statistics Theory · Mathematics 2010-08-18 Pedro A. Ortega

We consider the relation between knowledge and certainty, where a fact is known if it is true at all worlds an agent considers possible and is certain if it holds with probability 1. We identify certainty with probabilistic belief. We show…

Artificial Intelligence · Computer Science 2013-04-08 Joseph Y. Halpern

Although the notion of a concept as a collection of objects sharing certain properties, and the notion of a conceptual hierarchy are fundamental to both Formal Concept Analysis and Description Logics, the ways concepts are described and…

Logic in Computer Science · Computer Science 2011-07-15 Baris Sertkaya

The apparent failure of individual probabilistic expressions to distinguish uncertainty about truths from uncertainty about probabilistic assessments have prompted researchers to seek formalisms where the two types of uncertainties are…

Artificial Intelligence · Computer Science 2013-04-11 Judea Pearl

We propose an integration of possibility theory into non-classical logics. We obtain many formal results that generalize the case where possibility and necessity functions are based on classical logic. We show how useful such an approach is…

Artificial Intelligence · Computer Science 2013-02-28 Philippe Besnard , Jerome Lang

We develop a bottom-up approach to truth-value semantics for classical logic of partial terms based on equality and apply it to prove the conservativity of the addition of partial description and partial selection functions, independently…

Logic · Mathematics 2016-02-10 Franco Parlamento

Interpretability and explainability have gained more and more attention in the field of machine learning as they are crucial when it comes to high-stakes decisions and troubleshooting. Since both provide information about predictors and…

Machine Learning · Computer Science 2024-04-26 Benjamin Leblanc , Pascal Germain

In fuzzy propositional logic, to a proposition a partial truth in [0,1] is assigned. It is well known that under certain circumstances, fuzzy logic collapses to classical logic. In this paper, we will show that under dual conditions, fuzzy…

Artificial Intelligence · Computer Science 2007-05-23 Umberto Straccia

Agents' judgment depends on perception and previous knowledge. Assuming that previous knowledge depends on perception, we can say that judgment depends on perception. So, if judgment depends on perception, can agents judge that they have…

Neurons and Cognition · Quantitative Biology 2012-02-21 Ahmed M. Mahran

Our approach is basically a coherence approach, but we avoid the well-known pitfalls of coherence theories of truth. Consistency is replaced by reliability, which expresses support and attack, and, in principle, every theory (or agent,…

Artificial Intelligence · Computer Science 2018-04-03 Karl Schlechta

We review two strands of conceptual approaches to the formal representation of a decision maker's non-knowledge at the initial stage of a static one-person, one-shot decision problem in economic theory. One focuses on representations of…

General Finance · Quantitative Finance 2014-07-04 Ekaterina Svetlova , Henk van Elst

The main aim of this paper is to make a remark about the relation between (i) dualities between theories, as `duality' is understood in physics and (ii) equivalence of theories, as `equivalence' is understood in logic and philosophy. The…

History and Philosophy of Physics · Physics 2018-06-06 Jeremy Butterfield

The uncertainty principle is one of the characteristic properties of quantum theory based on incompatibility. Apart from the incompatible relation of quantum states, mutually exclusiveness is another remarkable phenomenon in the…

Quantum Physics · Physics 2016-11-09 Yunlong Xiao , Naihuan Jing

I review the relation of the Bell inequalities - characteristic of (classical) probabilities defined on Boolean logics - with noncontextual and local hidden variables theories of quantum mechanics and with quantum information.

Quantum Physics · Physics 2021-06-16 Emilio Santos

We explore uncertainty quantification in large language models (LLMs), with the goal to identify when uncertainty in responses given a query is large. We simultaneously consider both epistemic and aleatoric uncertainties, where the former…

Machine Learning · Computer Science 2024-07-18 Yasin Abbasi Yadkori , Ilja Kuzborskij , András György , Csaba Szepesvári
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