Extracting the K best solutions from a valued and-or acyclic graph

Abstract

In this thesis, we are interested in solving a problem that arises in model-based programming, specifically in the estimation of the state a system described by a probabilistic model. Some model-based estimators, such as the MEXEC algorithm and the DNNF-based Belief State Estimation algorithm, use a valued and-or acyclic graph to represent the possible estimates. These algorithms specifically use a valued smooth deterministic decomposable negation normal form (sd-DNNF) representation, a type of and-or acyclic graph. Prior work has focused on extracting either all or only the best solution from the sd-DNNF. This work develops an efficient algorithm that is able to extract the k best solutions, where k is a parameter to the algorithm. For a graph with -E- edges, -V - nodes and -Ev- children per non-leaf node, the algorithm presented in this thesis has a time complexity of O(-E-k log k +-E- log -Ev-+-V -k log -Ev-) and a space complexity O(-E-k).