The paper introduces an AND/OR search space perspective for graphical models that
include probabilistic networks (directed or undirected) and constraint networks. In contrast to …

Many real-world decision problems have multiple objectives. For example, when choosing a
medical treatment plan, we want to maximize the efficacy of the treatment, but also minimize …

Graphical models (eg, Bayesian and constraint networks, influence diagrams, and Markov
decision processes) have become a central paradigm for knowledge representation and …

In this article, we propose new algorithms for multi-objective coordination graphs (MO-
CoGs). Key to the efficiency of these algorithms is that they compute a convex coverage set …

Inspired by the recently introduced framework of AND/OR search spaces for graphical
models, we propose to augment Multi-Valued Decision Diagrams (MDD) with AND nodes, in …

Many real-world decision problems require making tradeoffs among multiple objectives.
However, in some cases, the relative importance of these objectives is not known when the …

Inference in expressive probabilistic models is generally intractable, which makes them
difficult to learn and limits their applicability. Sum-product networks are a class of deep …

We review a logic-based modeling language PRISM and report recent developments
including belief propagation by the generalized inside-outside algorithm and generative …

Decision making is hard. It often requires reasoning about uncertain environments, partial
observability and action spaces that are too large to enumerate. In such tasks decision …

The paper introduces mixed networks, a new graphical model framework for expressing and
reasoning with probabilistic and deterministic information. The motivation to develop mixed …