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Related papers: Belief Revision with Uncertain Inputs in the Possi…

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We introduce a graceful approach to probabilistic inference called bounded conditioning. Bounded conditioning monotonically refines the bounds on posterior probabilities in a belief network with computation, and converges on final…

Artificial Intelligence · Computer Science 2013-04-08 Eric J. Horvitz , Jaap Suermondt , Gregory F. Cooper

Belief revision of knowledge bases represented by a set of sentences in a given logic has been extensively studied but for specific logics, mainly propositional, and also recently Horn and description logics. Here, we propose to generalize…

Artificial Intelligence · Computer Science 2017-01-17 Marc Aiguier , Jamal Atif , Isabelle Bloch , Céline Hudelot

It is shown that the ability of the interval probability representation to capture epistemological independence is severely limited. Two events are epistemologically independent if knowledge of the first event does not alter belief (i.e.,…

Artificial Intelligence · Computer Science 2013-02-18 Lonnie Chrisman

According to Boutillier, Darwiche, Pearl and others, principles for iterated revision can be characterised in terms of changing beliefs about conditionals. For iterated contraction a similar formulation is not known. This is especially…

Artificial Intelligence · Computer Science 2022-02-03 Kai Sauerwald , Gabriele Kern-Isberner , Christoph Beierle

There are at least two ways to interpret numerical degrees of belief in terms of betting: (1) you can offer to bet at the odds defined by the degrees of belief, or (2) you can judge that a strategy for taking advantage of such betting…

Statistics Theory · Mathematics 2010-01-12 Glenn Shafer

Dempster's rule is a fundamental tool for combining belief functions from distinct and reliable sources. However, its intersection-based semantics imposes strong structural restrictions, which limits its flexibility in handling complex…

Artificial Intelligence · Computer Science 2026-05-19 Qianli Zhou , Ye Cui , Zhen Li , Witold Pedrycz , Yong Deng

Theory refinement is the task of updating a domain theory in the light of new cases, to be done automatically or with some expert assistance. The problem of theory refinement under uncertainty is reviewed here in the context of Bayesian…

Artificial Intelligence · Computer Science 2013-03-26 Wray L. Buntine

Given a set of probability measures $\mathcal{P}$ representing an agent's knowledge on the elements of a sigma-algebra $\mathcal{F}$, we can compute upper and lower bounds for the probability of any event $A\in\mathcal{F}$ of interest. A…

Statistics Theory · Mathematics 2023-05-09 Michele Caprio , Teddy Seidenfeld

This paper introduces the notions of independence and conditional independence in valuation-based systems (VBS). VBS is an axiomatic framework capable of representing many different uncertainty calculi. We define independence and…

Artificial Intelligence · Computer Science 2013-03-25 Prakash P. Shenoy

Inappropriate use of Dempster's rule of combination has led some authors to reject the Dempster-Shafer model, arguing that it leads to supposedly unacceptable conclusions when defaults are involved. A most classic example is about the…

Artificial Intelligence · Computer Science 2013-04-05 Philippe Smets , Yen-Teh Hsia

The idea of preserving conditional beliefs emerged recently as a new paradigm apt to guide the revision of epistemic states. Conditionals are substantially different from propositional beliefs and need specific treatment. In this paper, we…

Artificial Intelligence · Computer Science 2007-05-23 Gabriele Kern-Isberner

Revision programming is a formalism to describe and enforce updates of belief sets and databases. That formalism was extended by Fitting who assigned annotations to revision atoms. Annotations provide a way to quantify the confidence…

Artificial Intelligence · Computer Science 2007-05-23 Victor Marek , Inna Pivkina , Miroslaw Truszczynski

One of the most important aspects in any treatment of uncertain information is the rule of combination for updating the degrees of uncertainty. The theory of belief functions uses the Dempster rule to combine two belief functions defined by…

Artificial Intelligence · Computer Science 2013-04-05 Michael S. K. M. Wong , P. Lingras

When a mathematical or computational model is used to analyse some system, it is usual that some parameters resp.\ functions or fields in the model are not known, and hence uncertain. These parametric quantities are then identified by…

Probability · Mathematics 2016-07-01 Hermann G. Matthies , Elmar Zander , Bojana Rosic , Alexander Litvinenko

We view the syntax-based approaches to default reasoning as a model-based diagnosis problem, where each source giving a piece of information is considered as a component. It is formalized in the ATMS framework (each source corresponds to an…

Artificial Intelligence · Computer Science 2013-02-28 Jerome Lang

In this paper, an optimization problem with uncertain objective function coefficients is considered. The uncertainty is specified by providing a discrete scenario set, containing possible realizations of the objective function coefficients.…

Data Structures and Algorithms · Computer Science 2023-03-10 Marc Goerigk , Romain Guillaume , Adam Kasperski , Paweł Zieliński

We introduce a logic for temporal beliefs and intentions based on Shoham's database perspective. We separate strong beliefs from weak beliefs. Strong beliefs are independent from intentions, while weak beliefs are obtained by adding…

Artificial Intelligence · Computer Science 2016-04-27 Marc van Zee , Dragan Doder

Accepting a proposition means that our confidence in this proposition is strictly greater than the confidence in its negation. This paper investigates the subclass of uncertainty measures, expressing confidence, that capture the idea of…

Artificial Intelligence · Computer Science 2013-02-21 Didier Dubois , Henri Prade

This paper will focus on the process of 'fusing' several observations or models of uncertainty into a single resultant model. Many existing approaches to fusion use subjective quantities such as 'strengths of belief' and process these…

Artificial Intelligence · Computer Science 2020-07-28 Shawn C. Eastwood , Svetlana N. Yanushkevich

The problem of combining beliefs in the Dempster-Shafer belief theory has attracted considerable attention over the last two decades. The classical Dempster's Rule has often been criticised, and many alternative rules for belief combination…

Artificial Intelligence · Computer Science 2007-05-23 Audun Josang
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