Lazy Evaluation of Symmetric Bayesian Decision Problems
  Solving symmetric Bayesian decision problems is a computationally intensive
task to perform regardless of the algorithm used. In this paper we propose a
method for improving the efficiency of algorithms for solving Bayesian decision
problems. The method is based on the principle of lazy evaluation - a principle
recently shown to improve the efficiency of inference in Bayesian networks. The
basic idea is to maintain decompositions of potentials and to postpone
computations for as long as possible. The efficiency improvements obtained with
the lazy evaluation based method is emphasized through examples. Finally, the
lazy evaluation based method is compared with the hugin and valuation-based
systems architectures for solving symmetric Bayesian decision problems.
