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Admissible heuristic

About: Admissible heuristic is a research topic. Over the lifetime, 197 publications have been published within this topic receiving 15329 citations. The topic is also known as: admissible heuristics.


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Journal ArticleDOI
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

Proceedings Article
09 Aug 2003
TL;DR: This paper introduces and analyzes three new HS/DP algorithms, one of which approximates the latter by enforcing the consistency of the value function over the likely' reachable states only, and leads to great time and memory savings, with no much apparent loss in quality, when transitions have probabilities that differ greatly in value.
Abstract: Recent algorithms like RTDP and LAO* combine the strength of Heuristic Search (HS) and Dynamic Programming (DP) methods by exploiting knowledge of the initial state and an admissible heuristic function for producing optimal policies without evaluating the entire space. In this paper, we introduce and analyze three new HS/DP algorithms. A first general algorithm schema that is a simple loop in which 'inconsistent' reachable states (i.e., with residuals greater than a given c) are found and updated until no such states are found, and serves to make explicit the basic idea underlying HS/DP algorithms, leaving other commitments aside. A second algorithm, that builds on the first and adds a labeling mechanism for detecting solved states based on Tarjan's strongly-connected components procedure, which is very competitive with existing approaches. And a third algorithm, that approximates the latter by enforcing the consistency of the value function over the likely' reachable states only, and leads to great time and memory savings, with no much apparent loss in quality, when transitions have probabilities that differ greatly in value.

120 citations

Proceedings Article
04 Aug 1996
TL;DR: A general theory for the automatic discovery of heuristics based on considering multiple subgoals simultaneously is presented, and it is observed that as heuristic search problems are scaled up, more powerful heuristic functions become both necessary and cost-effective.
Abstract: We have found the first optimal solutions to random instances of the Twenty-Four Puzzle, the 5 × 5 version of the well-known sliding-tile puzzles. Our new contribution to this problem is a more powerful admissible heuristic function. We present a general theory for the automatic discovery of such heuristics, which is based on considering multiple subgoals simultaneously. In addition, we apply a technique for pruning duplicate nodes in depth-first search using a finitestate machine. Finally, we observe that as heuristic search problems are scaled up, more powerful heuristic functions become both necessary and cost-effective.

118 citations

Proceedings ArticleDOI
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

Proceedings ArticleDOI
28 Jul 2002
TL;DR: A plnning algorithm is described that integrates two approaches to solving Markov decision processes with large state spaces in a novel way that exploits symbolic model-checking techniques and demonstrates their usefulness for decision-theoretic planning.
Abstract: We describe a plnning algorithm that integrates two approaches to solving Markov decision processes with large state spaces. State abstraction is used to avoid evaluating states individually. Forward search from a start state, guided by an admissible heuristic, is used to avoid evaluating all states. We combine these two approaches in a novel way that exploits symbolic model-checking techniques and demonstrates their usefulness for decision-theoretic planning.

104 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20213
202015
201910
20183
20177
20167