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  1. Deduction, induction and probabilistic support.James Cussens - 1996 - Synthese 108 (1):1 - 10.
    Elementary results concerning the connections between deductive relations and probabilistic support are given. These are used to show that Popper-Miller's result is a special case of a more general result, and that their result is not very unexpected as claimed. According to Popper-Miller, a purely inductively supports b only if they are deductively independent — but this means that a b. Hence, it is argued that viewing induction as occurring only in the absence of deductive relations, as Popper-Miller sometimes do, (...)
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  2.  24
    Leibniz on Estimating the Uncertain: An English Translation of De incerti aestimatione with Commentary.Wolfgang David Cirilo de Melo & James Cussens - 2004 - The Leibniz Review 14:31-41.
    Leibniz’s De incerti aestimatione, which contains his solution to the division problem, has not received much attention, let alone much appreciation. This is surprising because it is in this work that the definition of probability in terms of equally possible cases appears for the first time. The division problem is used to establish and test probability theory; it can be stated as follows: if two players agree to play a game in which one has to win a certain number of (...)
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  3.  56
    Leibniz on Estimating the Uncertain.Wolfgang David Cirilo de Melo & James Cussens - 2004 - The Leibniz Review 14:31-41.
    Leibniz’s De incerti aestimatione, which contains his solution to the division problem, has not received much attention, let alone much appreciation. This is surprising because it is in this work that the definition of probability in terms of equally possible cases appears for the first time. The division problem is used to establish and test probability theory; it can be stated as follows: if two players agree to play a game in which one has to win a certain number of (...)
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  4.  3
    Integer Linear Programming for the Bayesian network structure learning problem.Mark Bartlett & James Cussens - 2017 - Artificial Intelligence 244 (C):258-271.
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  5.  5
    Inductive Logic Programming: 10th International Conference, ILP 2000, London, UK, July 24-27, 2000 Proceedings.James Cussens & Alan Frisch - 2000 - Springer.
    This book constitutes the refereed proceedings of the 10th International Conference on Inductive Logic Programming, ILP 2000, held in London, UK in July 2000 as past of CL 2000. The 15 revised full papers presented together with an invited paper were carefully reviewed and selected from 37 submissions. The papers address all current issues in inductive logic programming and inductive learning, from foundational aspects to applications in various fields like data mining, knowledge discovery, and ILP system design.
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  6.  11
    Integrating probabilistic and logical reasoning.James Cussens - 2001 - In David Corfield & Jon Williamson (eds.), Foundations of Bayesianism. Kluwer Academic Publishers. pp. 241--260.
  7.  2
    Integrating Probabilistic and Causal Reasoning.James Cussens - unknown
    We examine the vexed question of connections between logical and probabilistic reasoning. The reason for making such a connection are examined. We give an account of recent work which uses loglinear models to make the connection. We conclude with an analysis of various existing approaches combining logic and probability.
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  8.  21
    Probability, uncertainty and artificial intelligence: Carlotta Piscopo: The metaphysical nature of the non-adequacy claim. Dordrecht: Springer, 2013, 146pp, $129 HB.James Cussens - 2014 - Metascience 23 (3):505-511.
    The central thesis of this book is that the argument that probability is insufficient to handle uncertainty in artificial intelligence (AI) is metaphysical in nature. Piscopo calls this argument against probability the non-adequacy claim and provides this summary of it [which first appeared in (Piscopo and Birattari 2008)]:Probability theory is not suitable to handle uncertainty in AI because it has been developed to deal with intrinsically stochastic phenomena, while in AI, uncertainty has an epistemic nature. (Piscopo (3))Piscopo uses the term (...)
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  9.  14
    Leibniz on Estimating the Uncertain: An English Translation of De incerti aestimatione with Commentary.Wolfgang David Cirilo de Melo & James Cussens - 2004 - The Leibniz Review 14:31-41.
    Leibniz’s De incerti aestimatione, which contains his solution to the division problem, has not received much attention, let alone much appreciation. This is surprising because it is in this work that the definition of probability in terms of equally possible cases appears for the first time. The division problem is used to establish and test probability theory; it can be stated as follows: if two players agree to play a game in which one has to win a certain number of (...)
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  10.  3
    Learning Language in Logic.James Cussens & Saso Dzeroski - 2000 - Springer Verlag.
    The two-volume set LNCS 1842/1843 constitutes the refereed proceedings of the 6th European Conference on Computer Vision, ECCV 2000, held in Dublin, Ireland in June/July 2000. The 116 revised full papers presented were carefully selected from a total of 266 submissions. The two volumes offer topical sections on recognitions and modelling; stereoscopic vision; texture and shading; shape; structure from motion; image features; active, real-time, and robot vision; segmentation and grouping; vision systems engineering and evaluation; calibration; medical image understanding; and visual (...)
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