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.
Abstract
Inspired by game theory representations, Bayesian
networks, influence diagrams, structured Markov decision process
models, logic programming, and work in dynamical systems, the
independent choice logic (ICL) is a semantic framework that allows for
independent choices (made by various agents including nature) and a
logic program that gives the consequence of choices. This
representation can be used as a (runnable) specification for agents
that act in a world, make observations of that world and have memory,
as well as a modelling tool for dynamic environments with
uncertainty. The rules specify the consequences of an action, what can
be sensed and the utility of outcomes. This paper presents a
possible-worlds semantics, and shows how to embed influence diagrams,
structured Markov decision processes, and both the strategic (normal)
form and the extensive (game-tree) form of a game within the ICL. It's
argued that the ICL provides a natural and concise representation for
multi-agent decision-making under uncertainty that allows for the
representation of structured probability tables, the dynamic
construction of networks (through the use of logical variables) and a
way to handle uncertainty and decisions in a logical representation.
You canget the pdf or the
postscript. There there are also slides
(in PDF
format) from a talk "The Independent Choice Logic: A
pragmatic combination of logic and
decision theory", March 1998.
Related Papers
D. Poole, Abducing Through Negation As
Failure: Stable models in the Independent Choice Logic,
Journal of Logic Programming, 1999.
See also ongoing research. You can
get the
ICL code distribution.
Last updated 18 April 97 - David Poole