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Conditional independence is a crucial concept supporting adequate modelling and efficient reasoning in probabilistics. In knowledge representation, the idea of conditional independence has also been introduced for specific formalisms, such…

Artificial Intelligence · Computer Science 2024-12-19 Jesse Heyninck

Non-deductive reasoning systems are often {\em representation dependent}: representing the same situation in two different ways may cause such a system to return two different answers. Some have viewed this as a significant problem. For…

Artificial Intelligence · Computer Science 2007-05-23 Joseph Y. Halpern , Daphne Koller

Recently, it has been emphasized that the possibility theory framework allows us to distinguish between i) what is possible because it is not ruled out by the available knowledge, and ii) what is possible for sure. This distinction may be…

Artificial Intelligence · Computer Science 2013-01-07 Salem Benferhat , Didier Dubois , Souhila Kaci , Henri Prade

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

An ordinal view of independence is studied in the framework of possibility theory. We investigate three possible definitions of dependence, of increasing strength. One of them is the counterpart to the multiplication law in probability…

Artificial Intelligence · Computer Science 2013-02-28 Didier Dubois , Luis Farinas del Cerro , Andreas Herzig , Henri Prade

This paper presents an approach for developing the explanation capabilities of rule-based expert systems managing imprecise and uncertain knowledge. The treatment of uncertainty takes place in the framework of possibility theory where the…

Artificial Intelligence · Computer Science 2013-04-08 Henri Farrency , Henri Prade

In this paper the theory of flexibly-bounded rationality which is an extension to the theory of bounded rationality is revisited. Rational decision making involves using information which is almost always imperfect and incomplete together…

Artificial Intelligence · Computer Science 2013-06-11 Tshilidzi Marwala

In default reasoning, usually not all possible ways of resolving conflicts between default rules are acceptable. Criteria expressing acceptable ways of resolving the conflicts may be hardwired in the inference mechanism, for example…

Artificial Intelligence · Computer Science 2011-05-30 J. Rintanen

Rational inference relations were introduced by Lehmann and Magidor as the ideal systems for drawing conclusions from a conditional base. However, there has been no simple characterization of these relations, other than its original…

Logic in Computer Science · Computer Science 2007-05-23 Konstantinos Georgatos

This paper relates comparative belief structures and a general view of belief management in the setting of deductively closed logical representations of accepted beliefs. We show that the range of compatibility between the classical…

Artificial Intelligence · Computer Science 2013-02-01 Didier Dubois , Helene Fargier , Henri Prade

We propose a combination of probabilistic reasoning from conditional constraints with approaches to default reasoning from conditional knowledge bases. In detail, we generalize the notions of Pearl's entailment in system Z, Lehmann's…

Artificial Intelligence · Computer Science 2007-05-23 Thomas Lukasiewicz

Conditionals are useful for modelling, but are not always sufficiently expressive for capturing information accurately. In this paper we make the case for a form of conditional that is situation-based. These conditionals are more expressive…

Artificial Intelligence · Computer Science 2023-04-18 Giovanni Casini , Thomas Meyer , Ivan Varzinczak

In many situations humans have to reason with inconsistent knowledge. These inconsistencies may occur due to not fully reliable sources of information. In order to reason with inconsistent knowledge, it is not possible to view a set of…

Artificial Intelligence · Computer Science 2024-12-16 Nico Roos

Many writers have observed that default logics appear to contain the "lottery paradox" of probability theory. This arises when a default "proof by contradiction" lets us conclude that a typical X is not a Y where Y is an unusual subclass of…

Artificial Intelligence · Computer Science 2013-04-08 Eric Neufeld , J. D. Horton

It has been widely acknowledged that probabilistic independence and logical independence cannot be coherently reconciled. By bridging these two notions, this paper addresses three long-standing problems that have puzzled the field of…

Probability · Mathematics 2026-05-07 Chuanfeng Sun

Probability theory, epistemically interpreted, provides an excellent, if not the best available account of inductive reasoning. This is so because there are general and definite rules for the change of subjective probabilities through…

Artificial Intelligence · Computer Science 2013-04-10 Wolfgang Spohn

The lexicographic closure of any given finite set D of normal defaults is defined. A conditional assertion "if a then b" is in this lexicographic closure if, given the defaults D and the fact a, one would conclude b. The lexicographic…

Artificial Intelligence · Computer Science 2007-05-23 Daniel Lehmann

There is much interest in providing probabilistic semantics for defaults but most approaches seem to suffer from one of two problems: either they require numbers, a problem defaults were intended to avoid, or they generate peculiar side…

Artificial Intelligence · Computer Science 2013-04-10 Eric Neufeld , David L Poole

Theoretically as well as experimentally it is investigated how people represent their knowledge in order to make decisions or to share their knowledge with others. Experiment 1 probes into the ways how people 6ather information about the…

Artificial Intelligence · Computer Science 2013-04-15 Alf C. Zimmer

Possibilistic logic, an extension of first-order logic, deals with uncertainty that can be estimated in terms of possibility and necessity measures. Syntactically, this means that a first-order formula is equipped with a possibility degree…

Artificial Intelligence · Computer Science 2013-02-28 Bernhard Hollunder
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