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Richard J. Waldinger

Principal Scientist, Artificial Intelligence Center

Richard Waldinger, Ph.D., focuses on applying automatic deduction to artificial intelligence and software engineering. His research areas include program synthesis and verification, and planning. He has studied the use of deductive methods for natural-language question answering, and he is the co-author (with Zohar Manna) of a series of textbooks on the relationship between logic and computer programming.

Waldinger has been with the AIC since 1969 and has worked on theorem proving and its application to automated program synthesis and other problems on the border between software engineering and artificial intelligence. He also has served as a consulting professor of computer science at Stanford University.

He received his Ph.D. from Carnegie Mellon University under Herbert A. Simon. Waldinger is a recipient of an SRI Fellowship, a AAAI Fellowship, and a Herbrand Award.

Recent publications

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Natural Language Access: When Reasoning Makes Sense (7/27/2021) - We argue that to use natural language effectively, we must have both a deep understanding of the subject domain and…
What, Again? Automatic Deductive Synthesis of the Unification Algorithm (6/22/2021) - We describe work in progress towards deriving a unification algorithm automatically from a declarative specification using deductive methods.
Deductive Synthesis of the Unification Algorithm: The Automation of Introspection (11/1/2020) - We are working to create the first automatic deductive synthesis of a unification algorithm. The program is extracted from a…
Natural Language Access to Data: It Takes Common Sense! (3/1/2015) - In a deductive approach to this problem, language processing technology translates English queries into a first-order logical form, which is…
Deduction for Natural Language Access to Data (1/1/2014) - Knowledge and reasoning in a specific subject domain can greatly assist in natural language processing. This is demonstrated in the…
English Access to Structured Data (1/1/2011) - We present work on using a domain model to guide text interpretation, in the context of a project that aims…