Artificial Intelligence

Our vision of Artificial Intelligence was fairly simple. AI Systems must be able to perform efficient inference and tractable learning. We therefore defined two thrust areas: Inference and learning.

Parallel Inference Research

  • Parallel or Resource Limited Inference with Logical or Bayesian Network Representations
  • Parallel Matching or memory based inference
  • Parallel Inference in Logic Programs and Constraint Networks

We co-developed one of the earliest parallel deductive database systems (with Jack Minker, Madhur Kohli and others).

We derived efficient (sometimes theoretically optimal) algorithms for both matching and inference and in some cases proved lower bounds.

We also made an early proposal for Probabilistic Databases where the Database is able to perform inference in logarithmic time of the size of the stored representation (with Judea Pearl, Adam Grove and Arthur Delcher).



We worked on a broad range of problems in learning focusing on either delivering widely used systems or non-standard learning formalisms.

Some examples of this work include: