@inproceedings{maua2012uai,
 abstract = {Influence diagrams allow for intuitive and yet precise
description of complex situations involving decision
making under uncertainty. Unfortunately, most of the
problems described by influence diagrams are hard to
solve. In this paper we discuss the complexity of
approximately solving influence diagrams. We do not
assume no-forgetting or regularity, which makes the
class of problems we address very broad. Remarkably, we
show that when both the treewidth and the cardinality
of the variables are bounded the problem admits a fully
polynomial-time approximation scheme.},
 author = {Denis Deratani Mauá and Cassio Polpo de Campos and
Marco Zaffalon},
 booktitle = {Proceedings of the 28th Conference on Uncertainty in
Artificial Intelligence (UAI)},
 keywords = {decision networks,influence diagrams,combinatorial
optimization,bayesian networks},
 pages = {604--613},
 selected = {1},
 title = {The Complexity of Approximately Solving Influence
Diagrams},
 url = {http://www.auai.org/uai2012/papers/166.pdf},
 year = {2012}
}