2019 - Member of the National Academy of Engineering For methods of reasoning about knowledge, belief, and uncertainty and their applications to distributed computing and multiagent systems.
2015 - Fellow of the American Academy of Arts and Sciences
2008 - ACM AAAI Allen Newell Award For fundamental advances in reasoning about knowledge, belief, and uncertainty and their groundbreaking applications in artificial intelligence, computer science, game theory, economics, and the philosophy of science.
2005 - Fellow of the American Association for the Advancement of Science (AAAS)
2002 - ACM Fellow For contributions to the modeling of and reasoning about uncertainty.
1993 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For sustained excellence in theoretical research on the logics of and relationships among knowledge, common knowledge, belief and probability.
His scientific interests lie mostly in Artificial intelligence, Theoretical computer science, Mathematical economics, Knowledge representation and reasoning and Distributed computing. His Artificial intelligence study combines topics from a wide range of disciplines, such as Modal logic, Axiom, Semantics and Property. His work deals with themes such as Correctness, Computation, Secret sharing and Operator, which intersect with Theoretical computer science.
His Mathematical economics research is multidisciplinary, incorporating perspectives in Counterfactual conditional, Bayesian network, Relation and Rationality. He combines subjects such as Common knowledge, Procedural knowledge and Reasoning system with his study of Knowledge representation and reasoning. Joseph Y. Halpern works mostly in the field of Common knowledge, limiting it down to topics relating to Distributed knowledge and, in certain cases, Data science.
Joseph Y. Halpern mainly focuses on Mathematical economics, Theoretical computer science, Artificial intelligence, Discrete mathematics and Epistemology. Mathematical economics connects with themes related to Axiom in his study. His research investigates the connection between Theoretical computer science and topics such as Semantics that intersect with problems in Semantics.
His study in Non-monotonic logic and Knowledge representation and reasoning are all subfields of Artificial intelligence. His Discrete mathematics study frequently links to related topics such as Calculus. Particularly relevant to Best response is his body of work in Nash equilibrium.
Joseph Y. Halpern mainly investigates Mathematical economics, Causal model, Nash equilibrium, Probabilistic logic and Solution concept. His studies deal with areas such as Characterization, Bounded rationality and Rationality as well as Mathematical economics. His studies in Causal model integrate themes in fields like Epistemology, Causality, Outcome and Data science.
Joseph Y. Halpern has included themes like Value, Bayesian probability and Combinatorics in his Nash equilibrium study. His work carried out in the field of Probabilistic logic brings together such families of science as Simple, Theoretical computer science, Blockchain and Protocol. The True quantified Boolean formula research Joseph Y. Halpern does as part of his general Theoretical computer science study is frequently linked to other disciplines of science, such as Information acquisition, therefore creating a link between diverse domains of science.
His primary scientific interests are in Mathematical economics, Causal model, Causality, Epistemology and Game theory. His work on Sequential equilibrium and Nash equilibrium is typically connected to Causality and Natural as part of general Mathematical economics study, connecting several disciplines of science. The study incorporates disciplines such as Abstraction, Basis, Theoretical computer science, Probabilistic logic and Outcome in addition to Causal model.
His Theoretical computer science research incorporates themes from Transformation, Modal logic, Unary operation and Distribution. His Epistemology research includes themes of Autonomous agent and Epistemic modal logic. His Game theory study combines topics in areas such as Kullback–Leibler divergence, Rationality and Dilemma.
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Reasoning About Knowledge
Ronald Fagin;Joseph Y. Halpern;Moshe Y. Vardi;Yoram Moses.
“Sometimes” and “not never” revisited: on branching versus linear time temporal logic
E. Allen Emerson;Joseph Y. Halpern.
Journal of the ACM (1986)
Knowledge and common knowledge in a distributed environment
Joseph Y. Halpern;Yoram Moses.
Journal of the ACM (1990)
Reasoning about Uncertainty
Joseph Y. Halpern.
Belief, awareness, and limited reasoning
R. Fagin;J. Y. Halpern.
Artificial Intelligence (1987)
A guide to completeness and complexity for modal logics of knowledge and belief
Joseph Y. Halpern;Yoram Moses.
Artificial Intelligence (1992)
Gossip-based ad hoc routing
Zygmunt J. Haas;Joseph Y. Halpern;Li Li.
international conference on computer communications (2002)
An analysis of first-order logics of probability
Joseph Y. Halpern.
Artificial Intelligence (1990)
Causes and Explanations: A Structural-Model Approach. Part I: Causes
Joseph Y. Halpern;Judea Pearl.
The British Journal for the Philosophy of Science (2005)
A logic for reasoning about probabilities
R. Fagin;J.Y. Halpern;N. Megiddo.
logic in computer science (1988)
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