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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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Proceedings ArticleDOI
22 Apr 1996
TL;DR: A hybrid search algorithm for scheduling flexible manufacturing systems (FMS) that combines heuristic best-first strategy with controlled backtracking strategy and the execution of timed Petri nets to search for an optimal or near-optimal and deadlock-free schedule.
Abstract: This paper presents a hybrid search algorithm for scheduling flexible manufacturing systems (FMS). The algorithm combines heuristic best-first strategy with controlled backtracking strategy. Timed (place) Petri nets are used for problem representation. Their use allows to explicitly formulate concurrent activities, multiple resources sharing, precedence constraints and dynamic routing in FMS operation. The hybrid heuristic search algorithm is combined with the execution of the timed Petri nets to search for an optimal or near-optimal and deadlock-free schedule. The backtracking strategy is controllable. One can only employ the pure best-first search to obtain an optimal schedule thanks to a proposed admissible heuristic function. The presented method is illustrated through an FMS scheduling problem.

30 citations

Journal ArticleDOI
TL;DR: This paper shows how to format GPP as a search problem and introduces a sequence of admissible heuristic functions estimating the size of the optimal partition by looking into different interactions between vertices of the graph and achieves a speedup of up to a number of orders of magnitude.
Abstract: As search spaces become larger and as problems scale up, an efficient way to speed up the search is to use a more accurate heuristic function. A better heuristic function might be obtained by the following general idea. Many problems can be divided into a set of subproblems and subgoals that should be achieved. Interactions and conflicts between unsolved subgoals of the problem might provide useful knowledge which could be used to construct an informed heuristic function. In this paper we demonstrate this idea on the graph partitioning problem (GPP). We first show how to format GPP as a search problem and then introduce a sequence of admissible heuristic functions estimating the size of the optimal partition by looking into different interactions between vertices of the graph. We then optimally solve GPP with these heuristics. Experimental results show that our advanced heuristics achieve a speedup of up to a number of orders of magnitude. Finally, we experimentally compare our approach to other states of the art graph partitioning optimal solvers on a number of classes of graphs. The results obtained show that our algorithm outperforms them in many cases.

29 citations

Journal ArticleDOI
TL;DR: In this article, a heuristic search algorithm for solving first-order Markov Decision Processes (FOMDPs) is presented, where the search is restricted to those states that are reachable from the initial state.
Abstract: We present a heuristic search algorithm for solving first-order Markov Decision Processes (FOMDPs). Our approach combines first-order state abstraction that avoids evaluating states individually, and heuristic search that avoids evaluating all states. Firstly, in contrast to existing systems, which start with propositionalizing the FOMDP and then perform state abstraction on its propositionalized version we apply state abstraction directly on the FOMDP avoiding propositionalization. This kind of abstraction is referred to as first-order state abstraction. Secondly, guided by an admissible heuristic, the search is restricted to those states that are reachable from the initial state. We demonstrate the usefulness of the above techniques for solving FOMDPs with a system, referred to as FluCaP (formerly, FCPlanner), that entered the probabilistic track of the 2004 International Planning Competition (IPC'2004) and demonstrated an advantage over other planners on the problems represented in first-order terms.

29 citations

Journal ArticleDOI
TL;DR: An algorithm is presented which is shown to terminate with a most preferred path, given an admissible heuristic set, which illustrates how Artificial Intelligence techniques can be productively employed to solve multiobjective problems.

29 citations

Book ChapterDOI
26 Jul 2000
TL;DR: This work has learned how to accurately predict the running time of admissible heuristic search algorithms, as a function of the solution depth and the heuristic evaluation function.
Abstract: In the past several years, significant progress has been made in finding optimal solutions to combinatorial problems. In particular, random instances of both Rubik's Cube, with over 1019 states, andt he 5 × 5 sliding-tile puzzle, with almost 1025 states, have been solved optimally. This progress is not the result of better search algorithms, but more effective heuristic evaluation functions. In addition, we have learned how to accurately predict the running time of admissible heuristic search algorithms, as a function of the solution depth and the heuristic evaluation function. One corollary of this analysis is that an admissible heuristic function reduces the effective depth of search, rather than the effective branching factor.

29 citations


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