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Joseph Y. Halpern [64]Joseph Halpern [21]
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Joseph Y. Halpern
Cornell University
  1.  82
    Reasoning About Uncertainty.Joseph Y. Halpern - 2003 - MIT Press.
    Using formal systems to represent and reason about uncertainty.
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  2.  18
    Actual Causality.Joseph Halpern - 2016 - MIT Press.
    A new approach for defining causality and such related notions as degree of responsibility, degrees of blame, and causal explanation. Causality plays a central role in the way people structure the world; we constantly seek causal explanations for our observations. But what does it even mean that an event C "actually caused" event E? The problem of defining actual causation goes beyond mere philosophical speculation. For example, in many legal arguments, it is precisely what needs to be established in order (...)
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  3. Graded Causation and Defaults.Joseph Y. Halpern & Christopher Hitchcock - 2015 - British Journal for the Philosophy of Science 66 (2):413-457.
    Recent work in psychology and experimental philosophy has shown that judgments of actual causation are often influenced by consideration of defaults, typicality, and normality. A number of philosophers and computer scientists have also suggested that an appeal to such factors can help deal with problems facing existing accounts of actual causation. This article develops a flexible formal framework for incorporating defaults, typicality, and normality into an account of actual causation. The resulting account takes actual causation to be both graded and (...)
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  4. Causes and explanations: A structural-model approach. Part I: Causes.Joseph Y. Halpern & Judea Pearl - 2005 - British Journal for the Philosophy of Science 56 (4):843-887.
    We propose a new definition of actual causes, using structural equations to model counterfactuals. We show that the definition yields a plausible and elegant account of causation that handles well examples which have caused problems for other definitions and resolves major difficulties in the traditional account.
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  5.  41
    Belief, awareness, and limited reasoning.Ronald Fagin & Joseph Y. Halpern - 1987 - Artificial Intelligence 34 (1):39-76.
  6.  40
    A guide to completeness and complexity for modal logics of knowledge and belief.Joseph Y. Halpern & Yoram Moses - 1992 - Artificial Intelligence 54 (3):319-379.
  7. Actual causation and the art of modeling.Joseph Halpern & Christopher Hitchcock - 2010 - In Halpern Joseph & Hitchcock Christopher (eds.), Causality, Probability, and Heuristics: A Tribute to Judea Pearl. College Publications. pp. 383-406.
  8.  14
    An analysis of first-order logics of probability.Joseph Y. Halpern - 1990 - Artificial Intelligence 46 (3):311-350.
  9. Causes and Explanations: A Structural-Model Approach. Part II: Explanations.Joseph Y. Halpern & Judea Pearl - 2005 - British Journal for the Philosophy of Science 56 (4):889-911.
    We propose new definitions of (causal) explanation, using structural equations to model counterfactuals. The definition is based on the notion of actual cause, as defined and motivated in a companion article. Essentially, an explanation is a fact that is not known for certain but, if found to be true, would constitute an actual cause of the fact to be explained, regardless of the agent's initial uncertainty. We show that the definition handles well a number of problematic examples from the literature.
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  10.  18
    Dealing with logical omniscience: Expressiveness and pragmatics.Joseph Y. Halpern & Riccardo Pucella - 2011 - Artificial Intelligence 175 (1):220-235.
  11. Sleeping Beauty Reconsidered: Conditioning and Reflection in Asynchronous Systems.Joseph Halpern - 2004 - In Tamar Szabo Gendler & John Hawthorne (eds.), Proceedings of the Twentieth Conference on Uncertainty in Ai. Oxford University Press. pp. 111-142.
     
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  12.  52
    Appropriate causal models and the stability of causation.Joseph Y. Halpern - 2016 - Review of Symbolic Logic 9 (1):76-102.
  13.  9
    From statistical knowledge bases to degrees of belief.Fahiem Bacchus, Adam J. Grove, Joseph Y. Halpern & Daphne Koller - 1996 - Artificial Intelligence 87 (1-2):75-143.
  14. Sleeping Beauty Reconsidered: Conditioning and Reflection in Asynchronous Systems.Joseph Halpern - 2006 - Oxford Studies in Epistemology 1.
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  15. Should knowledge entail belief?Joseph Y. Halpern - 1996 - Journal of Philosophical Logic 25 (5):483 - 494.
    The appropriateness of S5 as a logic of knowledge has been attacked at some length in the philosophical literature. Here one particular attack based on the interplay between knowledge and belief is considered: Suppose that knowledge satisfies S5, belief satisfies KD45, and both the entailment property (knowledge implies belief) and positive certainty (if the agent believes something, she believes she knows it) hold. Then it can be shown that belief reduces to knowledge: it is impossible to have false beliefs. While (...)
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  16.  49
    Intransitivity and vagueness.Joseph Y. Halpern - 2008 - Review of Symbolic Logic 1 (4):530-547.
    There are many examples in the literature that suggest that indistinguishability is intransitive, despite the fact that the indistinguishability relation is typically taken to be an equivalence relation (and thus transitive). It is shown that if the uncertainty perception and the question of when an agent reports that two things are indistinguishable are both carefully modeled, the problems disappear, and indistinguishability can indeed be taken to be an equivalence relation. Moreover, this model also suggests a logic of vagueness that seems (...)
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  17.  17
    Reasoning about noisy sensors and effectors in the situation calculus.Fahiem Bacchus, Joseph Y. Halpern & Hector J. Levesque - 1999 - Artificial Intelligence 111 (1-2):171-208.
  18.  6
    The effect of bounding the number of primitive propositions and the depth of nesting on the complexity of modal logic.Joseph Y. Halpern - 1995 - Artificial Intelligence 75 (2):361-372.
  19.  63
    From causal models to counterfactual structures.Joseph Y. Halpern - 2013 - Review of Symbolic Logic 6 (2):305-322.
    Galles & Pearl (l998) claimed that s [possible-worlds] framework.s framework. Recursive models are shown to correspond precisely to a subclass of (possible-world) counterfactual structures. On the other hand, a slight generalization of recursive models, models where all equations have unique solutions, is shown to be incomparable in expressive power to counterfactual structures, despite the fact that the Galles and Pearl arguments should apply to them as well. The problem with the Galles and Pearl argument is identified: an axiom that they (...)
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  20. Sleeping Beauty Reconsidered: Conditioning and Reflection in Asynchronous Systems.Joseph Halpern - 2005 - In Tamar Szabo Gendler & John Hawthorne (eds.), Oxford Studies in Epistemology Volume 1. Oxford University Press UK.
     
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  21. Defining knowledge in terms of belief: The modal logic perspective.Joseph Y. Halpern, Dov Samet & Ella Segev - 2009 - Review of Symbolic Logic 2 (3):469-487.
    The question of whether knowledge is definable in terms of belief, which has played an important role in epistemology for the last 50 years, is studied here in the framework of epistemic and doxastic logics. Three notions of definability are considered: explicit definability, implicit definability, and reducibility, where explicit definability is equivalent to the combination of implicit definability and reducibility. It is shown that if knowledge satisfies any set of axioms contained in S5, then it cannot be explicitly defined in (...)
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  22.  11
    A nonstandard approach to the logical omniscience problem.Ronald Fagin, Joseph Y. Halpern & Moshe Y. Vardi - 1995 - Artificial Intelligence 79 (2):203-240.
  23.  80
    Compact Representations of Extended Causal Models.Joseph Y. Halpern & Christopher Hitchcock - 2013 - Cognitive Science 37 (6):986-1010.
    Judea Pearl (2000) was the first to propose a definition of actual causation using causal models. A number of authors have suggested that an adequate account of actual causation must appeal not only to causal structure but also to considerations of normality. In Halpern and Hitchcock (2011), we offer a definition of actual causation using extended causal models, which include information about both causal structure and normality. Extended causal models are potentially very complex. In this study, we show how it (...)
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  24.  21
    Reasoning About Knowledge: An Overview.Joseph Y. Halpern - 1988 - Journal of Symbolic Logic 53 (2):660-661.
  25.  5
    A logic to reason about likelihood.Joseph Y. Halpern & Michael O. Rabin - 1987 - Artificial Intelligence 32 (3):379-405.
  26.  10
    Two views of belief: belief as generalized probability and belief as evidence.Joseph Y. Halpern & Ronald Fagin - 1992 - Artificial Intelligence 54 (3):275-317.
  27.  38
    On the unusual effectiveness of logic in computer science.Joseph Y. Halpern, Robert Harper, Neil Immerman, Phokion G. Kolaitis, Moshe Y. Vardi & Victor Vianu - 2001 - Bulletin of Symbolic Logic 7 (2):213-236.
    In 1960, E. P. Wigner, a joint winner of the 1963 Nobel Prize for Physics, published a paper titled On the Unreasonable Effectiveness of Mathematics in the Natural Sciences [61]. This paper can be construed as an examination and affirmation of Galileo's tenet that “The book of nature is written in the language of mathematics”. To this effect, Wigner presented a large number of examples that demonstrate the effectiveness of mathematics in accurately describing physical phenomena. Wigner viewed these examples as (...)
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  28. What is an inference rule?Ronald Fagin, Joseph Y. Halpern & Moshe Y. Vardi - 1992 - Journal of Symbolic Logic 57 (3):1018-1045.
    What is an inference rule? This question does not have a unique answer. One usually finds two distinct standard answers in the literature; validity inference $(\sigma \vdash_\mathrm{v} \varphi$ if for every substitution $\tau$, the validity of $\tau \lbrack\sigma\rbrack$ entails the validity of $\tau\lbrack\varphi\rbrack)$, and truth inference $(\sigma \vdash_\mathrm{t} \varphi$ if for every substitution $\tau$, the truth of $\tau\lbrack\sigma\rbrack$ entails the truth of $\tau\lbrack\varphi\rbrack)$. In this paper we introduce a general semantic framework that allows us to investigate the notion of inference (...)
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  29.  26
    Common knowledge revisited.Ronald Fagin, Joseph Y. Halpern, Yoram Moses & Moshe Y. Vardi - 1999 - Annals of Pure and Applied Logic 96 (1-3):89-105.
  30.  8
    Modeling belief in dynamic systems, part I: Foundations.Nir Friedman & Joseph Y. Halpern - 1997 - Artificial Intelligence 95 (2):257-316.
  31.  78
    Updating Probability: Tracking Statistics as Criterion.Bas C. van Fraassen & Joseph Y. Halpern - 2016 - British Journal for the Philosophy of Science:axv027.
    ABSTRACT For changing opinion, represented by an assignment of probabilities to propositions, the criterion proposed is motivated by the requirement that the assignment should have, and maintain, the possibility of matching in some appropriate sense statistical proportions in a population. This ‘tracking’ criterion implies limitations on policies for updating in response to a wide range of types of new input. Satisfying the criterion is shown equivalent to the principle that the prior must be a convex combination of the possible posteriors. (...)
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  32. Oxford Studies in Epistemology.Joseph Halpern - 2004 - Oxford University Press.
     
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  33.  61
    Decision Theory with Resource‐Bounded Agents.Joseph Y. Halpern, Rafael Pass & Lior Seeman - 2014 - Topics in Cognitive Science 6 (2):245-257.
    There have been two major lines of research aimed at capturing resource-bounded players in game theory. The first, initiated by Rubinstein (), charges an agent for doing costly computation; the second, initiated by Neyman (), does not charge for computation, but limits the computation that agents can do, typically by modeling agents as finite automata. We review recent work on applying both approaches in the context of decision theory. For the first approach, we take the objects of choice in a (...)
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  34.  29
    Sufficient conditions for causality to be transitive.Joseph Y. Halpern - 2016 - Philosophy of Science 83 (2):213-226.
    Natural conditions are provided that are sufficient to ensure that causality as defined by approaches that use counterfactual dependence and structural equations will be transitive.
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  35.  16
    Probability and Conditionals: Belief Revision and Rational Decision.Joseph Y. Halpern, Ellery Eells & Brian Skyrms - 2000 - Philosophical Review 109 (2):277.
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  36.  7
    Updating Probability: Tracking Statistics as Criterion.Bas C. van Fraassen & Joseph Y. Halpern - 2017 - British Journal for the Philosophy of Science 68 (3):725-743.
    For changing opinion, represented by an assignment of probabilities to propositions, the criterion proposed is motivated by the requirement that the assignment should have, and maintain, the possibility of matching in some appropriate sense statistical proportions in a population. This ‘tracking’ criterion implies limitations on policies for updating in response to a wide range of types of new input. Satisfying the criterion is shown equivalent to the principle that the prior must be a convex combination of the possible posteriors. Furthermore, (...)
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  37.  9
    Great expectations. Part II: generalized expected utility as a universal decision rule.Francis C. Chu & Joseph Y. Halpern - 2004 - Artificial Intelligence 159 (1-2):207-229.
  38. Asymptotic conditional probabilities: The non-unary case.Adam J. Grove, Joseph Y. Halpern & Daphne Koller - 1996 - Journal of Symbolic Logic 61 (1):250-276.
    Motivated by problems that arise in computing degrees of belief, we consider the problem of computing asymptotic conditional probabilities for first-order sentences. Given first-order sentences φ and θ, we consider the structures with domain {1,..., N} that satisfy θ, and compute the fraction of them in which φ is true. We then consider what happens to this fraction as N gets large. This extends the work on 0-1 laws that considers the limiting probability of first-order sentences, by considering asymptotic conditional (...)
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  39.  29
    Weighted sets of probabilities and minimax weighted expected regret: a new approach for representing uncertainty and making decisions.Joseph Y. Halpern & Samantha Leung - 2015 - Theory and Decision 79 (3):415-450.
    We consider a setting where a decision maker’s uncertainty is represented by a set of probability measures, rather than a single measure. Measure-by-measure updating of such a set of measures upon acquiring new information is well known to suffer from problems. To deal with these problems, we propose using weighted sets of probabilities: a representation where each measure is associated with a weight, which denotes its significance. We describe a natural approach to updating in such a situation and a natural (...)
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  40.  12
    The bottleneck may be the solution, not the problem.Arnon Lotem, Oren Kolodny, Joseph Y. Halpern, Luca Onnis & Shimon Edelman - 2016 - Behavioral and Brain Sciences 39.
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  41.  98
    The Role of the Protocol in Anthropic Reasoning.Joseph Y. Halpern - 2015 - Ergo: An Open Access Journal of Philosophy 2:195-206.
    I show how thinking in terms of the protocol used can help clarify problems related to anthropic reasoning and self-location, such as the Doomsday Argument and the Sleeping Beauty Problem.
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  42.  6
    Levesque's axiomatization of only knowing is incomplete.Joseph Y. Halpern & Gerhard Lakemeyer - 1995 - Artificial Intelligence 74 (2):381-387.
  43.  11
    A small-trials PREE with adult humans: Resistance to extinction as a function of number of N-R transitions.Leonard Poon & Joseph Halpern - 1971 - Journal of Experimental Psychology 91 (1):124.
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  44.  22
    Causal Models with Constraints.Sander Beckers, Joseph Y. Halpern & Christopher Hitchcock - 2023 - Proceedings of the 2Nd Conference on Causal Learning and Reasoning.
    Causal models have proven extremely useful in offering formal representations of causal relationships between a set of variables. Yet in many situations, there are non-causal relationships among variables. For example, we may want variables LDL, HDL, and TOT that represent the level of low-density lipoprotein cholesterol, the level of lipoprotein high-density lipoprotein cholesterol, and total cholesterol level, with the relation LDL+HDL=TOT. This cannot be done in standard causal models, because we can intervene simultaneously on all three variables. The goal of (...)
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  45. Minimizing regret in dynamic decision problems.Joseph Y. Halpern & Samantha Leung - 2016 - Theory and Decision 81 (1):123-151.
    The menu-dependent nature of regret-minimization creates subtleties when it is applied to dynamic decision problems. It is not clear whether forgone opportunities should be included in the menu. We explain commonly observed behavioral patterns as minimizing regret when forgone opportunities are present. If forgone opportunities are included, we can characterize when a form of dynamic consistency is guaranteed.
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  46. First-order conditional logic for default reasoning revisited.Nir Friedman, Joseph Halpern, Koller Y. & Daphne - 2000 - Acm Trans. Comput. Logic 1 (2):175--207.
     
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  47.  10
    Executing Temporal Logic Programs.Joseph Y. Halpern & B. C. Moszkowski - 1988 - Journal of Symbolic Logic 53 (1):309.
  48.  48
    Presburger arithmetic with unary predicates is Π11 complete.Joseph Y. Halpern - 1991 - Journal of Symbolic Logic 56 (2):637 - 642.
    We give a simple proof characterizing the complexity of Presburger arithmetic augmented with additional predicates. We show that Presburger arithmetic with additional predicates is Π 1 1 complete. Adding one unary predicate is enough to get Π 1 1 hardness, while adding more predicates (of any arity) does not make the complexity any worse.
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  49.  22
    Transfer following regular and irregular sequences of events in a guessing situation.Lawrence S. Meyers, Erik Driessen & Joseph Halpern - 1972 - Journal of Experimental Psychology 92 (2):182.
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  50.  18
    A Causal Analysis of Harm.Sander Beckers, Hana Chockler & Joseph Y. Halpern - 2022 - Advances in Neural Information Processing Systems 35.
    As autonomous systems rapidly become ubiquitous, there is a growing need for a legal and regulatory framework to address when and how such a system harms someone. There have been several attempts within the philosophy literature to define harm, but none of them has proven capable of dealing with with the many examples that have been presented, leading some to suggest that the notion of harm should be abandoned and "replaced by more well-behaved notions". As harm is generally something that (...)
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