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Titlebook: Symbolic and Quantitative Approaches to Reasoning with Uncertainty; 11th European Confer Weiru Liu Conference proceedings 2011 Springer-Ver

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樓主: Lincoln
31#
發(fā)表于 2025-3-26 21:19:04 | 只看該作者
32#
發(fā)表于 2025-3-27 04:36:54 | 只看該作者
33#
發(fā)表于 2025-3-27 08:36:29 | 只看該作者
Backing and Undercutting in Defeasible Logic Programmingertheless, in D.LP is not possible to represent neither undercutting defeaters nor backings. The aim of this work is to extend the formalism of D.LP to allow attack and support for defeasible rules. Thus, it will be possible to build arguments for representing undercutting defeaters and backings.
34#
發(fā)表于 2025-3-27 11:34:08 | 只看該作者
35#
發(fā)表于 2025-3-27 16:37:21 | 只看該作者
36#
發(fā)表于 2025-3-27 19:07:42 | 只看該作者
SemCaDo: A Serendipitous Strategy for Learning Causal Bayesian Networks Using Ontologiesc information can be very useful to reveal new causal relations and provide the necessary knowledge to anticipate the optimal choice of experimentations. Our strategy also supports the evolving character of the semantic background by reusing the causal discoveries in order to enrich the domain ontologies.
37#
發(fā)表于 2025-3-27 22:25:08 | 只看該作者
Scaling Up the Greedy Equivalence Search Algorithm by Constraining the Search Space of Equivalence C, they recover a perfect map of the target distribution in the large sample limit. Moreover, experimental results confirm that, although they carry out much less computations, BNs learnt by those algorithms have the same quality as those learnt by GES.
38#
發(fā)表于 2025-3-28 02:52:56 | 只看該作者
39#
發(fā)表于 2025-3-28 07:09:37 | 只看該作者
Conference proceedings 2011 of reasoning and decision making under uncertainty; fuzzy sets and fuzzy logic; implementation and applications of uncertain systems; possibility theory and possibilistic logic; and uncertainty in databases..
40#
發(fā)表于 2025-3-28 11:08:50 | 只看該作者
Constructive Decision Theory: Short Summaryfers act . to act . iff the expected utility of . (with respect to Pr) is greater than that of .. Moreover, the probability measure is unique and the utility function is unique up to affine transformations. Similar representations of preference can be given with respect to other representations of uncertainty (see, for example, [2,5]).
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