Relevant bibliographies by topics / A* Heuristic Search

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'A* Heuristic Search'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "A* Heuristic Search"

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Wilt, Christopher, and Wheeler Ruml. "Speedy Versus Greedy Search." Proceedings of the International Symposium on Combinatorial Search 5, no. 1 (2021): 184–92. http://dx.doi.org/10.1609/socs.v5i1.18320.

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In work on satisficing search, there has been substantial attention devoted to how to solve problems associated with local minima or plateaus in the heuristic function. One technique that has been shown to be quite promising is using an alternative heuristic function that does not estimate cost-to-go, but rather estimates distance-to-go. Empirical results generally favor using the distance-to-go heuristic over the cost-to-go heuristic, but there is currently little beyond intuition to explain the difference. We begin by empirically showing that the success of the distance-to-go heuristic appea
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Siag, Lior, Ariel Felner, and Shahaf Shperberg. "Heuristics for Bounded-Suboptimal Search." Proceedings of the International Symposium on Combinatorial Search 18 (July 19, 2025): 145–53. https://doi.org/10.1609/socs.v18i1.35986.

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In heuristic search, it is well-established that different types of heuristics are suited for optimal heuristic search (OHS) and unbounded suboptimal search (USS). In OHS, the heuristic should minimize the error in estimating the true cost of the shortest path, whereas in USS, it is more beneficial for the heuristic to exhibit a clear gradient toward the goal, regardless of the error. However, no study has specifically investigated which heuristic is most effective for bounded suboptimal search (BSS), and the current standard is to use heuristics designed for OHS. This paper introduces a novel
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Wilt, Christopher, and Wheeler Ruml. "Effective Heuristics for Suboptimal Best-First Search." Journal of Artificial Intelligence Research 57 (October 31, 2016): 273–306. http://dx.doi.org/10.1613/jair.5036.

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Suboptimal heuristic search algorithms such as weighted A* and greedy best-first search are widely used to solve problems for which guaranteed optimal solutions are too expensive to obtain. These algorithms crucially rely on a heuristic function to guide their search. However, most research on building heuristics addresses optimal solving. In this paper, we illustrate how established wisdom for constructing heuristics for optimal search can fail when considering suboptimal search. We consider the behavior of greedy best-first search in detail and we test several hypotheses for predicting when
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Davidov, D., and S. Markovitch. "Multiple-Goal Heuristic Search." Journal of Artificial Intelligence Research 26 (August 25, 2006): 417–51. http://dx.doi.org/10.1613/jair.1940.

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This paper presents a new framework for anytime heuristic search where the task is to achieve as many goals as possible within the allocated resources. We show the inadequacy of traditional distance-estimation heuristics for tasks of this type and present alternative heuristics that are more appropriate for multiple-goal search. In particular, we introduce the marginal-utility heuristic, which estimates the cost and the benefit of exploring a subtree below a search node. We developed two methods for online learning of the marginal-utility heuristic. One is based on local similarity of the part
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Speck, David, Florian Geißer, and Robert Mattmüller. "When Perfect Is Not Good Enough: On the Search Behaviour of Symbolic Heuristic Search." Proceedings of the International Conference on Automated Planning and Scheduling 30 (June 1, 2020): 263–71. http://dx.doi.org/10.1609/icaps.v30i1.6670.

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Symbolic search has proven to be a competitive approach to cost-optimal planning, as it compactly represents sets of states by symbolic data structures. While heuristics for symbolic search exist, symbolic bidirectional blind search empirically outperforms its heuristic counterpart and is therefore the dominant search strategy. This prompts the question of why heuristics do not seem to pay off in symbolic search. As a first step in answering this question, we investigate the search behaviour of symbolic heuristic search by means of BDDA⋆. Previous work identified the partitioning of state sets
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Chen, Dillon Z., and Sylvie Thiébaux. "Novelty Heuristics, Multi-Queue Search, and Portfolios for Numeric Planning." Proceedings of the International Symposium on Combinatorial Search 17 (June 1, 2024): 203–7. http://dx.doi.org/10.1609/socs.v17i1.31559.

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Heuristic search is a powerful approach for solving planning problems and numeric planning is no exception. In this paper, we boost the performance of heuristic search for numeric planning with various powerful techniques orthogonal to improving heuristic informedness: numeric novelty heuristics, the Manhattan distance heuristic, and exploring the use of multi-queue search and portfolios for combining heuristics.
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Fišer, Daniel, Álvaro Torralba, and Jörg Hoffmann. "Operator-Potential Heuristics for Symbolic Search." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 9 (2022): 9750–57. http://dx.doi.org/10.1609/aaai.v36i9.21210.

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Symbolic search, using Binary Decision Diagrams (BDDs) to represent sets of states, is a competitive approach to optimal planning. Yet heuristic search in this context remains challenging. The many advances on admissible planning heuristics are not directly applicable, as they evaluate one state at a time. Indeed, progress using heuristic functions in symbolic search has been limited and even very informed heuristics have been shown to be detrimental. Here we show how this connection can be made stronger for LP-based potential heuristics. Our key observation is that, for this family of heurist
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Speck, David, André Biedenkapp, Frank Hutter, Robert Mattmüller, and Marius Lindauer. "Learning Heuristic Selection with Dynamic Algorithm Configuration." Proceedings of the International Conference on Automated Planning and Scheduling 31 (May 17, 2021): 597–605. http://dx.doi.org/10.1609/icaps.v31i1.16008.

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A key challenge in satisficing planning is to use multiple heuristics within one heuristic search. An aggregation of multiple heuristic estimates, for example by taking the maximum, has the disadvantage that bad estimates of a single heuristic can negatively affect the whole search. Since the performance of a heuristic varies from instance to instance, approaches such as algorithm selection can be successfully applied. In addition, alternating between multiple heuristics during the search makes it possible to use all heuristics equally and improve performance. However, all these approaches ign
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Fišer, Daniel, Álvaro Torralba, and Jörg Hoffmann. "Operator-Potentials in Symbolic Search: From Forward to Bi-directional Search." Proceedings of the International Conference on Automated Planning and Scheduling 32 (June 13, 2022): 80–89. http://dx.doi.org/10.1609/icaps.v32i1.19788.

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Symbolic search using binary decision diagrams is a state-of-the-art technique for cost-optimal planning. Heuristic search in this context has been problematic as even a very informative heuristic can be detrimental in case it induces difficult-to-represent state partitionings. It was recently shown that operator-potential heuristics can address this issue in forward search by computing a numeric potential for each operator corresponding to the change of the heuristic value induced by that operator. Forward search is, however, not the best known variant of symbolic search. Here we investigate
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Cichowicz, T., M. Drozdowski, M. Frankiewicz, G. Pawlak, F. Rytwinski, and J. Wasilewski. "Hyper-heuristics for cross-domain search." Bulletin of the Polish Academy of Sciences: Technical Sciences 60, no. 4 (2012): 801–8. http://dx.doi.org/10.2478/v10175-012-0093-7.

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Abstract In this paper we present two hyper-heuristics developed for the Cross-Domain Heuristic Search Challenge. Hyper-heuristics solve hard combinatorial problems by guiding low level heuristics, rather than by manipulating problem solutions directly. Two hyper-heuristics are presented: Five Phase Approach and Genetic Hive. Development paths of the algorithms and testing methods are outlined. Performance of both methods is studied. Useful and interesting experience gained in construction of the hyper-heuristics are presented. Conclusions and recommendations for the future advancement of hype
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Dissertations / Theses on the topic "A* Heuristic Search"

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Furcy, David Andre. "Speeding Up the Convergence of Online Heuristic Search and Scaling Up Offline Heuristic Search." Diss., Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/4855.

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The most popular methods for solving the shortest-path problem in Artificial Intelligence are heuristic search algorithms. The main contributions of this research are new heuristic search algorithms that are either faster or scale up to larger problems than existing algorithms. Our contributions apply to both online and offline tasks. For online tasks, existing real-time heuristic search algorithms learn better informed heuristic values and in some cases eventually converge to a shortest path by repeatedly executing the action leading to a successor state with a minimum cost-to-goal estimate.
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Jochumsson, Thorvaldur. "Heuristic multi-sequence search methods." Thesis, University of Skövde, Department of Computer Science, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-530.

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<p>With increasing size of sequence databases heuristic search approaches have become necessary. Hidden Markov models are the best performing search methods known today with respect to discriminative power, but are too time complex to be practical when searching in large sequence databases. In this report, heuristic algorithms that reduce the search space before searching with traditional search algorithms of hidden Markov models are presented and experimentally validated. The results of the validation show that the heuristic search algorithms will speed up the searches without decreasing thei
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Garrett, Caelan Reed. "Heuristic search for manipulation planning." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100596.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 61-64).<br>Manipulation problems involving many objects present substantial challenges for planning algorithms due to the high dimensionality and multi-modality of the search space. Symbolic task planners can efficiently con
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Richards, Simon Kim. "Symbolic bidirectional breadth-first heuristic search." Master's thesis, Mississippi State : Mississippi State University, 2004. http://library.msstate.edu/etd/show.asp?etd=etd-08302004-085304.

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Rodic, Daniel. "A Hybrid search heuristic-exhaustive search approach for rule extraction." Pretoria : [s.n.], 2000. http://upetd.up.ac.za/thesis/available/etd-05292006-110006/.

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Rolland, Erik. "Abstract heuristic search methods for graph partitioning." Connect to resource, 1991. http://rave.ohiolink.edu/etdc/view.cgi?acc%5Fnum=osu1262633923.

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Rolland, Eric. "Abstract heuristic search methods for graph partitioning." The Ohio State University, 1991. http://rave.ohiolink.edu/etdc/view?acc_num=osu1262633923.

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Rashid, Mahmood Abdur. "Heuristic Based Search for Protein Structure Prediction." Thesis, Griffith University, 2014. http://hdl.handle.net/10072/367134.

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Proteins that are essentially sequences of amino acids, adopt specific folded 3-dimensional (3D) structures to perform their specific tasks. However, misfolded proteins cause fatal diseases. Hence, protein structure prediction (PSP) has emerged as an important multi-disciplinary research problem. Given a protein sequence, the PSP problem is to find a 3D structure of the protein such that the total free energy amongst the amino acids in the sequence is minimised. In-vitro laboratory methods are time-consuming, expensive, and failure-prone. Conversely, computational methods are NP-hard even when
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Machemer, Kevin Scott 1958. "Heuristic search strategies on a dataflow computer." Thesis, The University of Arizona, 1990. http://hdl.handle.net/10150/291529.

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In recent years, sweeping changes in computer architecture have been transpiring as the technology continues to advance. As the physical limits for computational speed are approached, different design alternatives to the standard von Neumann architecture must be explored. The static dataflow architecture is one of the new possibilities being examined and involves a completely different concept in computation. By programming an irregular computational problem such as a heuristic search strategy on a static dataflow computer, its usefulness for solving difficult problems can be demonstrated. A s
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Henry, Obit Joe. "Developing novel meta-heuristic, hyper-heuristic and cooperative search for course timetabling problems." Thesis, University of Nottingham, 2010. http://eprints.nottingham.ac.uk/13581/.

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The research presented in this PhD thesis focuses on the problem of university course timetabling, and examines the various ways in which metaheuristics, hyperheuristics and cooperative heuristic search techniques might be applied to this sort of problem. The university course timetabling problem is an NP-hard and also highly constrained combinatorial problem. Various techniques have been developed in the literature to tackle this problem. The research work presented in this thesis approaches this problem in two stages. For the first stage, the construction of initial solutions or timetables,
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Books on the topic "A* Heuristic Search"

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Salhi, Saïd. Heuristic Search. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49355-8.

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Dasgupta, Pallab, P. P. Chakrabarti, and S. C. DeSarkar. Multiobjective Heuristic Search. Vieweg+Teubner Verlag, 1999. http://dx.doi.org/10.1007/978-3-322-86853-4.

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J, Rayward-Smith V., ed. Modern heuristic search methods. Wiley, 1996.

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Stefan, Schrödl, ed. Heuristic search: Theory and applications. Morgan Kaufmann, 2011.

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L, Aarts E. H., and Lenstra J. K, eds. Local search in combinatorial optimization. Princeton University Press, 2003.

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Schulz, Stephan. Leaning search control knowlledge for equational deduction. AKA, 2007.

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Schulz, Stephan. Leaning search control knowlledge for equational deduction. AKA, 2007.

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Tzeng, Chun-Hung. A Theory of Heuristic Information in Game-Tree Search. Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-61368-5.

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Tzeng, Chun-Hung. A Theory of Heuristic Information in Game-Tree Search. Springer Berlin Heidelberg, 1988.

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Tzeng, Chun-Hung. A theory of heuristic information in game-tree search. Springer-Verlag, 1988.

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Book chapters on the topic "A* Heuristic Search"

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Pirrone, Angelo, Peter C. R. Lane, Laura Bartlett, Noman Javed, and Fernand Gobet. "Heuristic Search of Heuristics." In Artificial Intelligence XL. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-47994-6_36.

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Salhi, Saïd. "Hybridisation Search." In Heuristic Search. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49355-8_5.

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Salhi, Saïd. "Introduction." In Heuristic Search. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49355-8_1.

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Salhi, Saïd. "Improvement-Only Heuristics." In Heuristic Search. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49355-8_2.

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Salhi, Saïd. "Not Necessary Improving Heuristics." In Heuristic Search. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49355-8_3.

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Salhi, Saïd. "Population-Based Heuristics." In Heuristic Search. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49355-8_4.

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Salhi, Saïd. "Implementation Issues." In Heuristic Search. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49355-8_6.

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Salhi, Saïd. "Applications, Conclusion and Research Challenges." In Heuristic Search. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49355-8_7.

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Chowdhary, K. R. "Heuristic Search." In Fundamentals of Artificial Intelligence. Springer India, 2020. http://dx.doi.org/10.1007/978-81-322-3972-7_9.

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Weik, Martin H. "heuristic search." In Computer Science and Communications Dictionary. Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_8334.

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Conference papers on the topic "A* Heuristic Search"

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Atzmon, Dor, Jiaoyang Li, Ariel Felner, et al. "Multi-Directional Heuristic Search." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/562.

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In the Multi-Agent Meeting problem (MAM), the task is to find a meeting location for multiple agents, as well as a path for each agent to that location. In this paper, we introduce MM*, a Multi-Directional Heuristic Search algorithm that finds the optimal meeting location under different cost functions. MM* generalizes the Meet in the Middle (MM) bidirectional search algorithm to the case of finding an optimal meeting location for multiple agents. Several admissible heuristics are proposed, and experiments demonstrate the benefits of MM*.
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Núñez-Molina, Carlos, Masataro Asai, Pablo Mesejo, and Juan Fernandez-Olivares. "On Using Admissible Bounds for Learning Forward Search Heuristics." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/747.

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In recent years, there has been growing interest in utilizing modern machine learning techniques to learn heuristic functions for forward search algorithms. Despite this, there has been little theoretical understanding of what they should learn, how to train them, and why we do so. This lack of understanding has resulted in the adoption of diverse training targets (suboptimal vs optimal costs vs admissible heuristics) and loss functions (e.g., square vs absolute errors) in the literature. In this work, we focus on how to effectively utilize the information provided by admissible heuristics in
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Hu, Shuli, and Nathan R. Sturtevant. "Direction-Optimizing Breadth-First Search with External Memory Storage." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/175.

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While computing resources have continued to grow, methods for building and using large heuristics have not seen significant advances in recent years. We have observed that direction-optimizing breadth-first search, developed for and used broadly in the Graph 500 competition, can also be applied for building heuristics. But, the algorithm cannot run efficiently using external memory -- when the heuristics being built are larger than RAM. This paper shows how to modify direction-optimizing breadth-first search to build external-memory heuristics. We show that the new approach is not effective in
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Li, Jiaoyang, Ariel Felner, Eli Boyarski, Hang Ma, and Sven Koenig. "Improved Heuristics for Multi-Agent Path Finding with Conflict-Based Search." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/63.

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Conflict-Based Search (CBS) and its enhancements are among the strongest algorithms for Multi-Agent Path Finding. Recent work introduced an admissible heuristic to guide the high-level search of CBS. In this work, we prove the limitation of this heuristic, as it is based on cardinal conflicts only. We then introduce two new admissible heuristics by reasoning about the pairwise dependencies between agents. Empirically, CBS with either new heuristic significantly improves the success rate over CBS with the recent heuristic and reduces the number of expanded nodes and runtime by up to a factor of
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Khalil, Elias B., Bistra Dilkina, George L. Nemhauser, Shabbir Ahmed, and Yufen Shao. "Learning to Run Heuristics in Tree Search." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/92.

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``Primal heuristics'' are a key contributor to the improved performance of exact branch-and-bound solvers for combinatorial optimization and integer programming. Perhaps the most crucial question concerning primal heuristics is that of at which nodes they should run, to which the typical answer is via hard-coded rules or fixed solver parameters tuned, offline, by trial-and-error. Alternatively, a heuristic should be run when it is most likely to succeed, based on the problem instance's characteristics, the state of the search, etc. In this work, we study the problem of deciding at which node a
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Middleton, Jaime, Rodrigo Toro Icarte, and Jorge Baier. "Real-Time Heuristic Search with LTLf Goals." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/663.

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In Real-Time Heuristic Search (RTHS) we are given a search graph G, a heuristic, and the objective is to find a path from a given start node to a given goal node in G. As such, one does not impose any trajectory constraints on the path, besides reaching the goal. In this paper we consider a version of RTHS in which temporally extended goals can be defined on the form of the path. Such goals are specified in Linear Temporal Logic over Finite Traces (LTLf), an expressive language that has been considered in many other frameworks, such as Automated Planning, Synthesis, and Reinforcement Learning,
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Chen, Jingwei, Robert C. Holte, Sandra Zilles, and Nathan R. Sturtevant. "Front-to-End Bidirectional Heuristic Search with Near-Optimal Node Expansions." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/69.

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It is well-known that any admissible unidirectional heuristic search algorithm must expand all states whose f-value is smaller than the optimal solution cost when using a consistent heuristic. Such states are called “surely expanded” (s.e.). A recent study characterized s.e. pairs of states for bidirectional search with consistent heuristics: if a pair of states is s.e. then at least one of the two states must be expanded. This paper derives a lower bound, VC, on the minimum number of expansions required to cover all s.e. pairs, and present a new admissible front-to-end bidirectional heuristic
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Cohen, Liron, Tansel Uras, Shiva Jahangiri, Aliyah Arunasalam, Sven Koenig, and T. K. Satish Kumar. "The FastMap Algorithm for Shortest Path Computations." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/198.

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We present a new preprocessing algorithm for embedding the nodes of a given edge-weighted undirected graph into a Euclidean space. The Euclidean distance between any two nodes in this space approximates the length of the shortest path between them in the given graph. Later, at runtime, a shortest path between any two nodes can be computed with an A* search using the Euclidean distances as heuristic. Our preprocessing algorithm, called FastMap, is inspired by the data-mining algorithm of the same name and runs in near-linear time. Hence, FastMap is orders of magnitude faster than competing appr
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Bercher, Pascal, Gregor Behnke, Daniel Höller, and Susanne Biundo. "An Admissible HTN Planning Heuristic." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/68.

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Hierarchical task network (HTN) planning is well-known for being an efficient planning approach. This is mainly due to the success of the HTN planning system SHOP2. However, its performance depends on hand-designed search control knowledge. At the time being, there are only very few domain-independent heuristics, which are designed for differing hierarchical planning formalisms. Here, we propose an admissible heuristic for standard HTN planning, which allows to find optimal solutions heuristically. It bases upon the so-called task decomposition graph (TDG), a data structure reflecting reachabl
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Allen, Cameron, Michael Katz, Tim Klinger, George Konidaris, Matthew Riemer, and Gerald Tesauro. "Efficient Black-Box Planning Using Macro-Actions with Focused Effects." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/554.

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The difficulty of deterministic planning increases exponentially with search-tree depth. Black-box planning presents an even greater challenge, since planners must operate without an explicit model of the domain. Heuristics can make search more efficient, but goal-aware heuristics for black-box planning usually rely on goal counting, which is often quite uninformative. In this work, we show how to overcome this limitation by discovering macro-actions that make the goal-count heuristic more accurate. Our approach searches for macro-actions with focused effects (i.e. macros that modify only a sm
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Reports on the topic "A* Heuristic Search"

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Evett, Matthew, James Hendler, Ambuj Mahanti, and Dana Nau. PRA: Massively Parallel Heuristic Search. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada454848.

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Lehnert, Wendy G. Case-Based Reasoning as a Paradigm for Heuristic Search. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada249334.

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Bekkerman, Ron, Shlomo Zilberstein, and James Allan. Web Page Clustering using Heuristic Search in the Web Graph. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada457111.

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Grimson, W. E. The Combinatorics of Heuristic Search Termination for Object Recognition in Cluttered Environments. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada209690.

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Yan, Yujie, and Jerome F. Hajjar. Automated Damage Assessment and Structural Modeling of Bridges with Visual Sensing Technology. Northeastern University, 2021. http://dx.doi.org/10.17760/d20410114.

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Recent advances in visual sensing technology have gained much attention in the field of bridge inspection and management. Coupled with advanced robotic systems, state-of-the-art visual sensors can be used to obtain accurate documentation of bridges without the need for any special equipment or traffic closure. The captured visual sensor data can be post-processed to gather meaningful information for the bridge structures and hence to support bridge inspection and management. However, state-of-the-practice data postprocessing approaches require substantial manual operations, which can be time-c
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6

Willson. L51756 State of the Art Intelligent Control for Large Engines. Pipeline Research Council International, Inc. (PRCI), 1996. http://dx.doi.org/10.55274/r0010423.

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Computers have become a vital part of the control of pipeline compressors and compressor stations. For many tasks, computers have helped to improve accuracy, reliability, and safety, and have reduced operating costs. Computers excel at repetitive, precise tasks that humans perform poorly - calculation, measurement, statistical analysis, control, etc. Computers are used to perform these type of precise tasks at compressor stations: engine / turbine speed control, ignition control, horsepower estimation, or control of complicated sequences of events during startup and/or shutdown. For other task
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