Logic, Knowledge Representation and Bayesian Decision Theory
Invited paper, First
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.
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.
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