When: Thursday, July 24
Time: 3:30pm
Where: 2310 CS
Speaker: Prof. David Page

Title: CLP(BN): Constraint Logic Programming for Probabilistic Knowledge

Joint work with Vitor Santos Costa, James Cussens and Maleeha Qazi
To be presented at the International Conference on Uncertainty in
Artificial Intelligence (UAI-03)

Abstract: In Datalog, missing values are represented by Skolem constants.
  More generally, in logic programming missing values, or
  existentially-quantified variables, are represented by terms built from
  Skolem functors.  In an analogy to probabilistic relational models (PRMs),
  we wish to represent the joint probability distribution over missing
  values in a database or logic program using a Bayesian network.  This
  paper presents an extension of logic programs that makes it possible
  to specify a joint probability distribution over terms built from
  Skolem functors in the program.  Our extension is based on constraint
  logic programming (CLP), so we call the extended language CLP(BN).
  We show that CLP(BN) subsumes PRMs.  We also show that algorithms
  from inductive logic programming (ILP) can be used with only minor
  modification to learn CLP(BN) programs.  An implementation of CLP(BN)
  is publicly available as part of the YAP Prolog system.