2011 Recipient: Judea Pearl

2011 Recipient: Judea Pearl

Dr. Judea Pearl has been a key researcher in the application of probabilistic methods to the understanding of intelligent systems, whether natural or artificial. He has pioneered the development of graphical models, and especially a class of graphical models known as Bayesian networks, which can be used to represent and to draw inferences from probabilistic knowledge in a highly transparent and computationally natural fashion. Graphical models have had a transformative impact across many disciplines, from statistics and machine learning to artificial intelligence; and they are the foundation of the recent emergence of Bayesian cognitive science. Dr. Pearl’s work can be seen as providing a rigorous foundation for a theory of epistemology which is not merely philosophically defensible, but which can be mathematically specified and computationally implemented. It also provides one of the most influential sources of hypotheses about the function of the human mind and brain in current cognitive science.

Dr. Pearl has further developed his work on graphical models to address one of the deepest challenges in philosophy and science: the analysis of causality. He has developed a calculus for reasoning about the causal structure of the world, which is able, for the first time, to give a precise analysis of the impact of interventions and how they combine with passive observations. He is able to interpret graphical models as providing a specification of the causal structure of a system, rather than merely providing a compact representation of a joint probability distribution. Given that our knowledge of the world is important primarily because it serves as the basis for action—i.e., for making interventions to the world which, we hope, will help achieve our goals, building a theory of causality is of central importance to understanding human cognition. Dr. Pearl’s path-breaking work has been enormously influential. In statistics, his work on causality has substantially contributed to the re-engagement of the statistical community with the problem of modeling causation, inferring causal structure from data, and pinpointing precisely the assumptions necessary for such inference. In philosophy, his analyses have provided a precise formulation, and elaboration, of previously informal theories of the nature of causality, counterfactual thinking, and interpretation of the natural language indicative and subjunctive conditionals, if-then, had-it-been, and if-it-were-not-for. Moreover, Dr. Pearl’s work on causality has helped reinvigorate causality research in cognitive science, leading to a wide variety of models and experiments.

Dr Pearl’s academic career began in electrical engineering. He has a Bachelors degree in Electrical Engineering from the Technion – Israel Institute of Technology (1960); a Masters degree in Physics from Rutgers University (1965); and Ph.D. degree in Electrical Engineering from the Polytechnic Institute of Brooklyn (1965). He worked at RCA Research Laboratories, Princeton, New Jersey, on superconductive parametric and storage devices, and at Electronic Memories, Inc., Hawthorne, California, on advanced memory systems, before joining UCLA in 1970, where he is currently Director of the Cognitive Systems Laboratory in the Department of Computer Science.

He has written over 350 publications, including three highly influential books. The first, Heuristics (1984) provided an analysis and overview of heuristic methods for domains including planning, problem-solving, scheduling, and optimization, with particular reference to applications in artificial intelligence and operations research. His second book, Probabilistic Reasoning in Intelligent Systems (1988), outlined his seminal work on graphical models for the representation of, and reasoning with, probabilistic knowledge and uncertain evidence. His third book, Causality: Models, Reasoning, and Inference (2000), summarized his breakthrough research on representing, and making inferences about, causal and counterfactual relationships.

Dr. Pearl has previously been awarded a number of distinctions and honors. He is a Fellow of the Institute for Electronics and Electrical Engineers, and the Association for the Advancement of Artificial Intelligence, and a Member of the National Academy of Engineering, and he received an honorary doctorate from the University of Toronto in 2007. He has received major awards recognizing the impact of his research across a number of disciplines, including the Award for Research Excellence from the International Joint Conferences on Artificial Intelligence (1999), the Classic Paper Award from the Association for the Advancement of Artificial Intelligence (2000), the Lakatos Award for distinguished contributions to the philosophy of science (2001), the Association for Computing Machinery’s Allen Newell Award for outstanding contributions to computer science (2003), and the Benjamin Franklin Medal in Computers and Cognitive Science (2008).

Selected Publications

  • Pearl, J. (1984). Heuristics. Reading, MA: Addison-Wesley.
  • Pearl, J. (1986). Fusion, Propagation and Structuring in Belief Networks. Artificial Intelligence, 29, 241 – 288.
  • Dechter, R. & Pearl, J. (1987). Network-Based Heuristics for Constraint-Satisfaction Problems. Artificial Intelligence, 34, 1 – 38.
  • Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems, San Mateo, CA and : Morgan-Kaufmann.
  • Dechter, R. & Pearl, J. (1989) Tree-Clustering Schemes for Constraint-Processing. Artificial Intelligence, 38, 353 – 366.
  • Pearl, J. & Verma, T. S. (1991). A Theory of Inferred Causation. In J.A Allen, R. Fikes, and E. Sandewall (Eds.), Principles of Knowledge Representation and Reasoning: Proceeding of the Second International Conference, San Mateo, CA: Morgan Kaufmann, 441 – 452.
  • Dechter, R., Meiri, I. & J. Pearl, J. (1991). Temporal Constraint Networks, Artificial Intelligence, 49, 61 – 95.
  • Verma, T. & Pearl, J. (1991). Equivalence and Synthesis of Causal Models. In P. Bonissone, M. Henrion, L. N. Kanal & J. F. Lemmer (Eds.), Uncertainty in Artificial Intelligence 6, Cambridge, MA, Elsevier Science Publishers, 225 – 268.
  • Pearl, J. (1995). Causal Diagrams for Empirical Research, Biometrika, 82, 669 – 709.
  • Pearl, J. (2000). Causality: Models, Reasoning, and Inference, Cambridge, UK: Cambridge University Press.
  • Halpern, J. Y. & Pearl, J. (2005). Causes and explanations: A structural-model approach—Part I: Causes. British Journal of Philosophy of Science, 56, 843 – 887.
  • Halpern, J. Y. & Pearl, J. (2005). Causes and explanations: A structural-model approach—Part II: Explanations. British Journal of Philosophy of Science, 56, 889 – 911.
  • Shpitser, I. & J. Pearl, J. (2008). Complete Identification Methods for the Causal Hierarchy, Journal of Machine Learning Research, 9, 1941 – 1979.
  • Pearl, J. (2009). Causal inference in statistics: An overview. Statistics Surveys, 3, 96 – 146.