2.2k citations
- California Institute of TechnologyUS14 papers
- University of California, BerkeleyUS7 papers
- University of OxfordGB7 papers
- Brookhaven National LaboratoryUS6 papers
- Centre National de la Recherche ScientifiqueFR6 papers
- Jet Propulsion LaboratoryUS5 papers
- Max Planck Institute for Dynamics and Self-OrganizationDE5 papers
- Norwegian University of Science and TechnologyNO5 papers
- Princeton UniversityUS5 papers
- The University of TokyoJP5 papers
- University of California San DiegoUS5 papers
- University of CambridgeGB5 papers
7 papers · 1 filter
A Game-Theoretic Analysis of Updating Sets of Probabilities
Peter D. Grunwald, Joseph Y. Halpern
We consider how an agent should update her uncertainty when it is represented by a set of probability distributions and the agent observes that a random variable takes on v…
Sleeping Beauty Reconsidered: Conditioning and Reflection in Asynchronous Systems
Joseph Y. Halpern
A careful analysis of conditioning in the Sleeping Beauty problem is done, using the formal model for reasoning about knowledge and probability developed by Halpern and Tuttle. Whi…
Representation Dependence in Probabilistic Inference
Joseph Y. Halpern, Daphne Koller
Non-deductive reasoning systems are often {\em representation dependent}: representing the same situation in two different ways may cause such a system to return two different answ…
Responsibility and blame: a structural-model approach
Hana Chockler, Joseph Y. Halpern
Causality is typically treated an all-or-nothing concept; either A is a cause of B or it is not. We extend the definition of causality introduced by Halpern and Pearl [2001] to tak…
Great Expectations. Part II: Generalized Expected Utility as a Universal Decision Rule
Francis C. Chu, Joseph Y. Halpern
Many different rules for decision making have been introduced in the literature. We show that a notion of generalized expected utility proposed in Part I of this paper is a univers…
Modeling Belief in Dynamic Systems, Part I: Foundations
Nir Friedman, Joseph Y. Halpern
Belief change is a fundamental problem in AI: Agents constantly have to update their beliefs to accommodate new observations. In recent years, there has been much work on axiomatic…