Probabilistic Horn abduction and Bayesian networks
In Artificial Intelligence, Volume 64, Numbers 1,
pages 81-129, 1993.
This paper presents a simple framework for Horn-clause abduction, with
probabilities associated with hypotheses. The framework incorporates
assumptions about the rule base and independence assumptions amongst
hypotheses. It is shown how any probabilistic knowledge representable
in a discrete Bayesian belief network can be represented in this
framework. The main contribution is in finding a relationship between
logical and probabilistic notions of evidential reasoning. This
provides a useful representation language in its own right, providing
a compromise between heuristic and epistemic adequacy. It also shows
how Bayesian networks can be extended beyond a propositional language.
This paper also shows how a language with only
(unconditionally) independent hypotheses can represent any
probabilistic knowledge, and argues that it is better to invent new
hypotheses to explain dependence rather than having to worry about
dependence in the language.
You can get the pdf.
D. Poole, ``Logic
Programming, Abduction and Probability: a top-down anytime algorithm
for estimating prior and posterior probabilities'', New
Generation Computing, 11(3-4), 377-400, 1993.
described there is available, along with the example programs
described in the AI Journal paper.
Probabilistic Horn abduction has been developed further in the
independent choice logic. See:
D. Poole, The Independent Choice Logic for
modelling multiple agents under uncertainty.
D. Poole, Abducing Through Negation As
Failure: Stable models in the Independent Choice Logic.
To put this work in perspective, you should see
a description of my probabilistic research and ongoing research.
Last updated 6 Sept 98 - David Poole