Showing papers on "Admissible heuristic published in 2001"
TL;DR: The analysis allows us to accurately predict the performance of IDA ∗ on actual problems such as the sliding-tile puzzles and Rubik's Cube, and shows that the asymptotic heuristic branching factor is the same as the brute-force branching factor.
150 citations
07 Jul 2001
TL;DR: This paper describes an efficient A* search algorithm for statistical machine translation and develops various so-phisticated admissible and almost admissible heuristic functions that allow to translate even long sentences.
Abstract: In this paper, we describe an efficient A* search algorithm for statistical machine translation. In contrary to beam-search or greedy approaches it is possible to guarantee the avoidance of search errors with A*. We develop various so-phisticated admissible and almost admissible heuristic functions. Especially our newly developped method to perform a multi-pass A* search with an iteratively improved heuristic function allows us to translate even long sentences. We compare the A* search algorithm with a beam-search approach on the Hansards task.
117 citations
01 Jan 2001
TL;DR: An algorithm for planning with time and resources, based on heuristic search, that minimizes makespan using an admissible heuristic derived automatically from the problem instance and develops a planner that combines expressivity and performance.
Abstract: We present an algorithm for planning with time and resources based on heuristic search. The algorithm minimizes makespan using an admissible heuristic derived automatically from the problem instance. Estimators for resource consumption are derived in the same way. The goals are twofold: to show the flexibility of the heuristic search approach to planning and to develop a planner that combines expressivity and performance. Two main issues are the definition of regression in a temporal setting and the definition of the heuristic estimating completion time. A number of experiments are presented for assessing the performance of the resulting planner.
79 citations
04 Mar 2001
TL;DR: Two new algorithms for optimal pairwise sequence alignment are presented which outperform traditional methods on very large problem instances (hundreds of thousands of characters, for example).
Abstract: Sequence alignment is an important operation in computational biology. Both dynamic programming and A* heuristic search algorithms for optimal sequence alignment are discussed and evaluated Presented here are two new algorithms for optimal pairwise sequence alignment which outperform traditional methods on very large problem instances (hundreds of thousands of characters, for example). The technique combines the benefits of dynamic programming and A* heuristic search, with a minimal amount of additional overhead. The dynamic programming matrix is traversed along antidiagonals, bounding the computation to exclude portions of the matrix that cannot contain optimal paths. An admissible heuristic assists in pruning away unnecessary areas of the matrix, while preserving optimal solutions for any given scoring function. Since memory requirements are a major concern for large sequence alignment problems, it is shown how the standard algorithm (requiring quadratic space) can be reformulated as a divide and conquer algorithm (requiring only linear space, at the cost of some recomputuation).
25 citations