The Cognitive Science Society today announces that Professor Judea Pearl of University of California at Los Angeles (UCLA) has received the 2010 Rumelhart Prize for his leading research in Artificial Intelligence (AI) and systems that reason plausibly from uncertain evidence. His work on graphical models addresses one of the deepest challenges in philosophy and science: The dynamics of beliefs and the analysis of causality. Graphical models have had a transformative impact across many disciplines—including statistics, machine learning, epidemiology and psychology—and they are the foundation of the recent emergence of a branch of cognitive science representing probabilistic relationships, such as those between symptoms and diseases, and skills and earnings.
“Dr. Pearl’s path-breaking work has been enormously influential. He provides one of the most prominent hypotheses about the workings of the human mind, and has helped reinvigorate causality research,” said William Bechtel, Rumelhart Prize committee member and philosophy professor at the University of California at San Diego. “People often say, ‘You can’t derive causation from correlation.’ But Dr. Pearl’s research shows that you can logically determine causal relations from correlations if you have many interrelated variables and you make some minimal assumptions about how causal processes operate. The Cognitive Science Society is proud to recognize our esteemed colleague and the very high bar he has set with his research achievements.”
“Given that our knowledge of the world is important primarily because it serves as the basis for action, building a theory of causality is, I believe, of central importance to understanding human cognition,” said Dr. Pearl. “The inspiration for my works came from cognitive science and from the 1970’s papers of David Rumelhart, while the applicability of my research is in part thanks to collaboration I’ve received within the robotics, statistical and epidemiology communities. I’m honored to receive the Rumelhart Prize and accept this recognition in the spirit of continued collaboration with other facets of science that are helping solve the ever-fascinating mysteries of how the mind works.”
About Dr. Judea Pearl
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, including a class of graphical models known as Bayesian networks, which can be used to represent and draw inferences from probabilistic knowledge in a highly transparent and computationally efficient way. A version of Baysian network also forms the basis for modern methods of causal and counterfactual inferences. He has a bachelor’s degree in electrical engineering from the Technion – Israel Institute of Technology (1960); a master’s degree in physics from Rutgers University (1965); and Ph.D. degree in electrical engineering from the Polytechnic Institute of Brooklyn (1965). He has written more than 350 publications including three highly influential books, Heuristics (1984), Probabilistic Reasoning in Intelligent Systems (1988), and Causality: Models, Reasoning, and Inference (2000).
About the Cognitive Science Society
The Cognitive Science Society, Inc. is a non-profit professional organization that brings together researchers from many fields who hold a common goal: understanding the nature of the human mind. The Society promotes scientific interchange among researchers in disciplines comprising the field of Cognitive Science, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy and Education.
About the Rumelhart Prize
The David E. Rumelhart Prize is awarded annually to an individual or collaborative team making a significant, contemporary contribution to the theoretical foundations of human cognition. The prize consists of a certificate, a citation of the awardee’s contribution and a monetary award of $100,000. The prize is sponsored by the Robert J. Glushko and Pamela Samuelson Foundation.
