Titlebook: Symbolic and Quantitative Approaches to Reasoning with Uncertainty; 17th European Confer Zied Bouraoui,Srdjan Vesic Conference proceedings

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A Logical Framework for User-Feedback Dialogues on Hypotheses in Weighted Abductionterminate under certain reasonable conditions and achieve a fixed target hypothesis if the user determines the positivity or negativity of each pointed-out property based on whether the target hypothesis has that property.

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Modifications of the Miller Definition of Contrastive (Counterfactual) Explanationslternative unified definition that is modular with respect to an underlying definition of non-contrastive explanations. To the best of our knowledge this paper also provides the first explicit comparison between the original and modified HP definitions.

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Alveoli

On Identifiability of BN2A Networksility is an important concept because without it we cannot hope to learn correct models. We present necessary conditions for the identifiability of BN2As with Noisy-AND models. We also propose and test a numerical approach for testing identifiability.

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Approximations of System W Between c-Inference, System Z, and Lexicographic Inferencehermore, we develop approximations that extend system W and introduce an inductive inference operator that strictly extends system W and that is strictly extended by lexicographic inference. This leads to a map of inference relations between rational closure and c-inference on the one side and lexic

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Conference proceedings 2024 ECSQARU 2023, held in Arras, France, in September 2023.. The 35 full papers presented in this volume were carefully reviewed and selected from 46 submissions. The papers are organized in topical sections about Complexity and Database Theory; Formal Concept Analysis: Theoretical Advances; Formal Con

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Cautious Decision-Making for Tree Ensemblesuce the risk of making wrong decisions. In this paper, we propose two cautious decision-making procedures, by aggregating trees providing probability intervals constructed via the imprecise Dirichlet model. The trees are aggregated in the belief functions framework, by maximizing the lower expected

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