USA - 2008
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.
Joe Halpern's investigations into the logics of knowledge, belief, probability, and causality have had a major impact in artificial intelligence, computer science, game theory, economics, and the philosophy of science. His work addresses the fundamental question of what information states are attainable by communicating agents. The insights from this groundbreaking research have, for instance, radically changed our understanding of what information states are (un)reachable by communicating agents, of how iterated knowledge and beliefs characterize the rationality of participants in a game, of how to model degrees of belief rigorously when the background knowledge includes both uncertain and certain propositions, leading to a statistically sound model of default reasoning; and of how to model causality and degrees of responsibility and blame.
Professor Halpern has not only led the way linking scientifically multiple fields, but his example has been followed by many other researchers leading to the current thriving interactions between knowledge representation, probabilistic reasoning, applied logic, theoretical computer science, and game theory. His leadership goes back to founding the conference on Theoretical Aspects of Rationality and Knowledge, which continues to bring together researchers in AI, computer science, game theory, philosophy, and linguistics, and continues through the education of students and other researchers in how to translate between the languages and methods of different disciplines. As a member of the ACM Publication Board, editor-in-chief of the Journal of the ACM, and founder of CoRR (the Computing Research Repository), Professor Halpern has also played a leading role in increasing access to scientific publications in all areas of computer science.
USA - 2002
For contributions to the modeling of and reasoning about uncertainty.