We present a new algorithm for exactly solving decision making problems represented as
influence diagrams. We do not require the usual assumptions of no forgetting and regularity; …

We describe multi-objective influence diagrams, based on a set of p objectives, where utility
values are vectors in Rp, and are typically only partially ordered. These can still be solved by …

Two important tasks in probabilistic reasoning are the computation of the maximum posterior
probability of a given subset of the variables in a Bayesian network (MAP), and the …

Influence diagrams allow for intuitive and yet precise description of complex situations
involving decision making under uncertainty. Unfortunately, most of the problems described …

Limited memory influence diagrams are graph-based models that describe decision
problems with limited information such as planning with teams and/or agents with imperfect …

Influence diagrams (ID) and limited memory influence diagrams (LIMID) are flexible tools to
represent discrete stochastic optimization problems, with the Markov decision process …

Influence diagrams are intuitive and concise representations of structured decision
problems. When the problem is non-Markovian, an optimal strategy can be exponentially …

A limited-memory influence diagram (LIMID) generalizes a traditional influence diagram by
relaxing the assumptions of regularity and no-forgetting, allowing a wider range of decision …

This article discusses the current state of the art in terms of computational complexity for the
problem of finding the most probable configuration of a subset of variables in a multivariate …

This thesis develops algorithms for stochastic optimization problems such as Markov
Decision Processes (MDPs) or Partially Observable Markov Decision Processes (POMDPs) …