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  1. Kevin B. Korb, Erik P. Nyberg & Lucas Hope (2011). A New Causal Power Theory. In Phyllis McKay Illari, Federica Russo & Jon Williamson (eds.), Causality in the Sciences. Oup Oxford.
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  2. Charles R. Twardy & Kevin B. Korb (2011). Actual Causation by Probabilistic Active Paths. Philosophy of Science 78 (5):900-913.
    We present a probabilistic extension to active path analyses of token causation (Halpern & Pearl 2001, forthcoming; Hitchcock 2001). The extension uses the generalized notion of intervention presented in (Korb et al. 2004): we allow an intervention to set any probability distribution over the intervention variables, not just a single value. The resulting account can handle a wide range of examples. We do not claim the account is complete --- only that it fills an obvious gap in previous active-path approaches. (...)
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  3. Toby Handfield, Charles R. Twardy, Kevin B. Korb & Graham Oppy (2008). The Metaphysics of Causal Models: Where's the Biff? Erkenntnis 68 (2):149-68.
    This paper presents an attempt to integrate theories of causal processes—of the kind developed by Wesley Salmon and Phil Dowe—into a theory of causal models using Bayesian networks. We suggest that arcs in causal models must correspond to possible causal processes. Moreover, we suggest that when processes are rendered physically impossible by what occurs on distinct paths, the original model must be restricted by removing the relevant arc. These two techniques suffice to explain cases of late preëmption and other cases (...)
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  4. Kevin B. Korb (2007). Jon Williamson. Bayesian Nets and Causality: Philosophical and Computational Foundations. Philosophia Mathematica 15 (3):389-396.
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  5. Kevin B. Korb & Erik Nyberg (2006). The Power of Intervention. Minds and Machines 16 (3):289-302.
    We further develop the mathematical theory of causal interventions, extending earlier results of Korb, Twardy, Handfield, & Oppy, (2005) and Spirtes, Glymour, Scheines (2000). Some of the skepticism surrounding causal discovery has concerned the fact that using only observational data can radically underdetermine the best explanatory causal model, with the true causal model appearing inferior to a simpler, faithful model (cf. Cartwright, (2001). Our results show that experimental data, together with some plausible assumptions, can reduce the space of viable explanatory (...)
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  6. Kevin B. Korb (2004). Introduction: Machine Learning as Philosophy of Science. Minds and Machines 14 (4):433-440.
    I consider three aspects in which machine learning and philosophy of science can illuminate each other: methodology, inductive simplicity and theoretical terms. I examine the relations between the two subjects and conclude by claiming these relations to be very close.
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  7. Charles R. Twardy & Kevin B. Korb (2004). A Criterion of Probabilistic Causation. Philosophy of Science 71 (3):241-262.
    The investigation of probabilistic causality has been plagued by a variety of misconceptions and misunderstandings. One has been the thought that the aim of the probabilistic account of causality is the reduction of causal claims to probabilistic claims. Nancy Cartwright (1979) has clearly rebutted that idea. Another ill-conceived idea continues to haunt the debate, namely the idea that contextual unanimity can do the work of objective homogeneity. It cannot. We argue that only objective homogeneity in combination with a causal interpretation (...)
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  8. Charles R. Twardy, Kevin B. Korb, Ole Rogeberg, Cristina Bicchieri, John Duffy, Gil Tolle, P. D. Magnus, Craig Callender, Joseph F. Hanna & Paul Skokowski (2004). 1. A Criterion of Probabilistic Causation A Criterion of Probabilistic Causation (Pp. 241-262). Philosophy of Science 71 (3).
     
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  9. Kevin B. Korb & Ann E. Nicholson (2000). The Essential Roles of Emotion in Cognitive Architecture. Behavioral and Brain Sciences 23 (2):205-206.
    Rolls's presentation of emotion as integral to cognition is a welcome counter to a long tradition of treating them as antagonists. His eduction of experimental evidence in support of this view is impressive. However, we find his excursion into the philosophy of consciousness less successful. Rolls gives syntactical manipulation the central role in consciousness (in stark contrast to Searle, for whom “mere” syntax inevitably falls short of consciousness), and leaves us wondering about the roles left for emotion after all.
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  10. Kevin B. Korb (1999). Probabilistic Causal Structure. In Howard Sankey (ed.), Causation and Laws of Nature. Kluwer. 265--311.
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  11. Kevin B. Korb (1998). The Frame Problem: An AI Fairy Tale. [REVIEW] Minds and Machines 8 (3):317-351.
    I analyze the frame problem and its relation to other epistemological problems for artificial intelligence, such as the problem of induction, the qualification problem and the "general" AI problem. I dispute the claim that extensions to logic (default logic and circumscriptive logic) will ever offer a viable way out of the problem. In the discussion it will become clear that the original frame problem is really a fairy tale: as originally presented, and as tools for its solution are circumscribed by (...)
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  12. Kevin B. Korb & Jonathan J. Oliver (1998). A Refutation of the Doomsday Argument. Mind 107 (426):403-410.
    Carter and Leslie's Doomsday Argument maintains that reflection upon the number of humans born thus far, when that number is viewed as having been uniformly randomly selected from amongst all humans, past, present and future, leads to a dramatic rise in the probability of an early end to the human experiment. We examine the Bayesian structure of the Argument and find that the drama is largely due to its oversimplification.
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  13. Kevin B. Korb & Chris S. Wallace (1997). In Search of the Philosopher's Stone: Remarks on Humphreys and Freedman's Critique of Causal Discovery. British Journal for the Philosophy of Science 48 (4):543-553.
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  14. Kevin B. Korb (1994). Stephen Jay Gould on Intelligence. Cognition 52 (2):111-123.
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  15. J. David Smith, Deborah G. Kemler, Lisa A. Grohskopf Nelson, Terry Appleton, Mary K. Mullen, Judy S. Deloache, Nancy M. Burns, Kevin B. Korb, Robert L. Goldstone & Jean E. Andruski (1994). STEVEN A. SLOMAN (Brown University, Providence) When Explanations Compete: The Role of Explanatory Coherence on Judgements of Likelihood, 1-21. Cognition 52 (251):251.
     
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  16. Kevin B. Korb (1992). The Collapse of Collective Defeat: Lessons From the Lottery Paradox. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1992:230 - 236.
    The Lottery Paradox has been thought to provide a reductio argument against probabilistic accounts of inductive inference. As a result, much work in artificial intelligence has concentrated on qualitative methods of inference, including default logics, which are intended to model some varieties of inductive inference. It has recently been shown that the paradox can be generated within qualitative default logics. However, John Pollock's qualitative system of defeasible inference (named OSCAR), does avoid the Lottery Paradox by incorporating a rule designed (...)
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  17. Kevin B. Korb (1991). Causation and Universals. Review of Metaphysics 45 (2):397-399.
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  18. Kevin B. Korb (1991). Explaining Science. British Journal for the Philosophy of Science 42 (2):239-253.
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  19. Kevin B. Korb (1991). Searle's AI Program. Journal of Experimental and Theoretical Artificial Intelligence 3:283-96.
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