http://amturing.acm.org/award_winners/pearl_2658896.cfm
CITATION:
For fundamental contributions to artificial intelligence through the
development of a calculus for probabilistic and causal reasoning.

"Judea Pearl's work has transformed artificial intelligence (AI) by
creating a representational and computational foundation for the
processing of information under uncertainty.  Pearl's work went beyond
both the logic-based theoretical orientation of AI and its rule-based
technology for expert systems.  He identified uncertainty as a core
problem faced by intelligent systems and developed an algorithmic
interpretation of probability theory as an effective foundation for
the representation and acquisition of knowledge.

Focusing on conditional independence as an organizing principle for
capturing structural aspects of probability distributions, Pearl
showed how graph theory can be used to characterize conditional
independence, and invented message-passing algorithms that exploit
graphical structure to perform probabilistic reasoning effectively.
This breakthrough has had major impact on a wide variety of fields
where the restriction to simplified models had severely limited the
scope of probabilistic methods; examples include natural language
processing, speech processing, computer vision, robotics,
computational biology, and error-control coding.

Equally significant is Pearl's work on causal reasoning, where he
developed a graph-based calculus of interventions that makes it
possible to derive causal knowledge from the combined effects of
actions and observations. This work has been transformative within AI
and computer science, and has had major impact on allied disciplines
of economics, philosophy, psychology, sociology, and statistics."
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