Logic, Knowledge Representation and Bayesian Decision Theory

David Poole

Invited paper, First International Conference on Computational Logic (CL2000), London, July 2000.


In this paper I give a brief overview of recent work on uncertainty in AI, and relate it to logical representations. Bayesian decision theory and logic are both normative frameworks for reasoning that emphasize different aspects of intelligent reasoning. Belief networks (Bayesian networks) are representations of independence that form the basis for understanding much of the recent work on reasoning under uncertainty, evidential and causal reasoning, decision analysis, dynamical systems, optimal control, reinforcement learning and Bayesian learning. The independent choice logic provides a bridge between logical representations and belief networks that lets us understand these other representations and their relationship to logic and shows how they can extended to first-order rule-based representations. This paper discusses what the representations of uncertainty can bring to the computational logic community and what the computational logic community can bring to those studying reasoning under uncertainty.

You can get the paper and the slides from the invited talk.

Related Papers

David Poole, The Independent Choice Logic for modelling multiple agents under uncertainty. In Artificial Intelligence, Volume 94, Numbers 1-2, Special Issue on Economic Principles of Multi-agent Systems, pages 5-56, 1997.

D. Poole, Abducing Through Negation As Failure: Stable models in the Independent Choice Logic, to appear Journal of Logic Programming, 1999.

See also ongoing research. You can get the ICL code distribution.

Last updated 1 March 2001 - David Poole