Rong Zhou

Sweep A: space-efficient heuristic search in partially ordered graphs

We describe a novel heuristic search algorithm, called Sweep A*, that exploits the regular structure of partially ordered graphs to substantially reduce the memory requirements of search. We show that it outperforms previous search algorithms in optimally aligning multiple protein or DNA sequences, an important problem in bioinformatics. Sweep A* also promises to be effective for other search problems with similar structure.

Symbolic Heuristic Search Using Decision Diagrams

We show how to use symbolic model-checking techniques in heuristic search algorithms for both deterministic and decision-theoretic planning problems. A symbolic approach exploits state abstraction by using decision diagrams to compactly represent sets of states and operators on sets of states. In earlier work, symbolic model-checking techniques have been used to find plans that minimize the number of steps needed to reach a goal. Our approach generalizes this by showing how to find plans that minimize the expected cost of reaching a goal. For this generalization, we use algebraic decision diagrams instead of binary decision diagrams. In particular, we show that algebraic decision diagrams provide a compact representation of state evaluation functions. We describe symbolic generalizations of A* search for deterministic planning and of LAO* search for decision-theoretic planning problems formalized as Markov decision processes.We report experimental results and discuss issues for future work.

K-group A* for multiple sequence alignment with quasi-natural gap costs

Alignment of multiple protein or DNA sequences is an important problem in bioinformatics. Previous work has shown that the A* search algorithm can find optimal alignments for up to several sequences, and that a K-group generalization of A* can find approximate alignments for much larger numbers of sequences [T. Ikeda et al. (1999)]. In this paper, we describe the first implementation of K-group A* that uses quasinatural gap costs, the cost model used in practice by biologists. We also introduce a new method for computing gap-opening costs in profile alignment. Our results show that K-group A* can efficiently find optimal or close-to-optimal alignments for small groups of sequences, and, for large numbers of sequences, it can find higher-quality alignments than the widely-used CLUSTAL family of approximate alignment tools. This demonstrates the benefits of A* in aligning large numbers of sequences, as typically compared by biologists, and suggests that K-group A* could become a practical tool for multiple sequence alignment.