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  1. Norman Fenton, Martin Neil & David A. Lagnado (2013). A General Structure for Legal Arguments About Evidence Using Bayesian Networks. Cognitive Science 37 (1):61-102.
    A Bayesian network (BN) is a graphical model of uncertainty that is especially well suited to legal arguments. It enables us to visualize and model dependencies between different hypotheses and pieces of evidence and to calculate the revised probability beliefs about all uncertain factors when any piece of new evidence is presented. Although BNs have been widely discussed and recently used in the context of legal arguments, there is no systematic, repeatable method for modeling legal arguments as BNs. Hence, where (...)
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  2. Jr: Henry E. Kyburg (1990). Probabilistic Inference and Probabilistic Reasoning. Philosophical Topics 18 (2):107-116.
  3. Stephen Leeds (1994). A Note on Pollock's System of Direct Inference. Theory and Decision 36 (3):247-256.
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  4. Thomas Lukasiewicz (2005). Nonmonotonic Probabilistic Reasoning Under Variable-Strength Inheritance with Overriding. Synthese 146 (1-2):153 - 169.
    We present new probabilistic generalizations of Pearl’s entailment in System Z and Lehmann’s lexicographic entailment, called Zλ- and lexλ-entailment, which are parameterized through a value λ ∈ [0,1] that describes the strength of the inheritance of purely probabilistic knowledge. In the special cases of λ = 0 and λ = 1, the notions of Zλ- and lexλ-entailment coincide with probabilistic generalizations of Pearl’s entailment in System Z and Lehmann’s lexicographic entailment that have been recently introduced by the author. We show (...)
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  5. Andrés Páez (2006). Explanations in K: An Analysis of Explanation as a Belief Revision Operation. Athena Verlag.
    Explanation and understanding are intimately connected notions, but the nature of that connection has generally not been considered a topic worthy of serious philosophical investigation. Most authors have avoided making reference to the notion of understanding in their accounts of explanation because they fear that any mention of the epistemic states of the individuals involved compromises the objectivity of explanation. Understanding is a pragmatic notion, they argue, and pragmatics should be kept at a safe distance from the universal features of (...)
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  6. Niki Pfeifer (2013). The New Psychology of Reasoning: A Mental Probability Logical Perspective. Thinking and Reasoning 19 (3-4):329-345.
  7. Guy Politzer & Laure Carles (2001). Belief Revision and Uncertain Reasoning. Thinking and Reasoning 7 (3):217 – 234.
    When a new piece of information contradicts a currently held belief, one has to modify the set of beliefs in order to restore its consistency. In the case where it is necessary to give up a belief, some of them are less likely to be abandoned than others. The concept of epistemic entrenchment is used by some AI approaches to explain this fact based on formal properties of the belief set (e.g., transitivity). Two experiments were designed to test the hypothesis (...)
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  8. Raghav Ramachandran, Arthur Ramer & Abhaya C. Nayak (2012). Probabilistic Belief Contraction. Minds and Machines 22 (4):325-351.
    Probabilistic belief contraction has been a much neglected topic in the field of probabilistic reasoning. This is due to the difficulty in establishing a reasonable reversal of the effect of Bayesian conditionalization on a probabilistic distribution. We show that indifferent contraction, a solution proposed by Ramer to this problem through a judicious use of the principle of maximum entropy, is a probabilistic version of a full meet contraction. We then propose variations of indifferent contraction, using both the Shannon entropy measure (...)
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