Mixtures of Deterministic-Probabilistic Networks and their AND/OR Search Space
This provides a new framework for AI and graphical models, enabling more efficient reasoning in domains with both probabilistic and deterministic components.
The paper tackles the problem of reasoning with probabilistic and deterministic information by introducing mixed networks, which combine belief and constraint networks, and develops a linear space search algorithm based on an AND/OR search space, resulting in exponential effectiveness in pruning the search space when constraints are tractable.
The paper introduces mixed networks, a new framework for expressing and reasoning with probabilistic and deterministic information. The framework combines belief networks with constraint networks, defining the semantics and graphical representation. We also introduce the AND/OR search space for graphical models, and develop a new linear space search algorithm. This provides the basis for understanding the benefits of processing the constraint information separately, resulting in the pruning of the search space. When the constraint part is tractable or has a small number of solutions, using the mixed representation can be exponentially more effective than using pure belief networks which odel constraints as conditional probability tables.