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  1. Vision, knowledge, and the mystery link.John L. Pollock & Iris Oved - 2005 - Noûs 39 (1):309-351.
    Imagine yourself sitting on your front porch, sipping your morning coffee and admiring the scene before you. You see trees, houses, people, automobiles; you see a cat running across the road, and a bee buzzing among the flowers. You see that the flowers are yellow, and blowing in the wind. You see that the people are moving about, many of them on bicycles. You see that the houses are painted different colors, mostly earth tones, and most are one-story but a (...)
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  • The logical foundations of goal-regression planning in autonomous agents.John L. Pollock - 1998 - Artificial Intelligence 106 (2):267-334.
  • ``Defeasible Reasoning with Variable Degrees of Justification".John L. Pollock - 2001 - Artificial Intelligence 133 (1-2):233-282.
    The question addressed in this paper is how the degree of justification of a belief is determined. A conclusion may be supported by several different arguments, the arguments typically being defeasible, and there may also be arguments of varying strengths for defeaters for some of the supporting arguments. What is sought is a way of computing the “on sum” degree of justification of a conclusion in terms of the degrees of justification of all relevant premises and the strengths of all (...)
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  • Causal Probability.John L. John L. - 2002 - Synthese 132 (1/2):143-185.
    Examples growing out of the Newcomb problem have convinced many people that decision theory should proceed in terms of some kind of causal probability. I endorse this view and define and investigate a variety of causal probability. My definition is related to Skyrms' definition, but proceeds in terms of objective probabilities rather than subjective probabilities and avoids taking causal dependence as a primitive concept.
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  • Towards a formal account of reasoning about evidence: Argumentation schemes and generalisations. [REVIEW]Floris Bex, Henry Prakken, Chris Reed & Douglas Walton - 2003 - Artificial Intelligence and Law 11 (2-3):125-165.
    This paper studies the modelling of legal reasoning about evidence within general theories of defeasible reasoning and argumentation. In particular, Wigmore's method for charting evidence and its use by modern legal evidence scholars is studied in order to give a formal underpinning in terms of logics for defeasible argumentation. Two notions turn out to be crucial, viz. argumentation schemes and empirical generalisations.
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  • Self-stabilizing defeat status computation: dealing with conflict management in multi-agent systems.Pietro Baroni, Massimiliano Giacomin & Giovanni Guida - 2005 - Artificial Intelligence 165 (2):187-259.
  • SCC-recursiveness: a general schema for argumentation semantics.Pietro Baroni, Massimiliano Giacomin & Giovanni Guida - 2005 - Artificial Intelligence 168 (1-2):162-210.
  • Extending abstract argumentation systems theory.P. Baroni, M. Giacomin & G. Guida - 2000 - Artificial Intelligence 120 (2):251-270.
  • Automata for infinite argumentation structures.Pietro Baroni, Federico Cerutti, Paul E. Dunne & Massimiliano Giacomin - 2013 - Artificial Intelligence 203 (C):104-150.