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Artículos de revistas sobre el tema "Heuristic"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 25 de julio de 2025

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1

Seipp, Jendrik. "Better Orders for Saturated Cost Partitioning in Optimal Classical Planning." Proceedings of the International Symposium on Combinatorial Search 8, no. 1 (2021): 149–53. http://dx.doi.org/10.1609/socs.v8i1.18438.

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Cost partitioning is a general method for adding multiple heuristic values admissibly. In the setting of optimal classical planning, saturated cost partitioning has recently been shown to be the cost partitioning algorithm of choice for pattern database heuristics found by hill climbing, systematic pattern database heuristics and Cartesian abstraction heuristics. To evaluate the synergy of the three heuristic types, we compute the saturated cost partitioning over the combined sets of heuristics and observe that the resulting heuristic is outperformed by the heuristic that simply maximizes over

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2

Sanggala, Ekra, and Muhammad Ardhya Bisma. "Perbandingan Savings Algorithm dengan Nearest Neighbour dalam Menyelesaikan Russian TSP Instances." Jurnal Media Teknik dan Sistem Industri 7, no. 1 (2023): 27. http://dx.doi.org/10.35194/jmtsi.v7i1.3039.

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Travelling Salesman Problem (TSP) is the problem for finding the shortest route starting from start node then visiting number of nodes exactly once and finally go back to start node. Several heuristics are popular for solving TSP, for example Savings Algorithm and Nearest Neighbour. Performance heuristics on solving TSP are diverse, so there is need of reference for choosing a heuristic. Comparing heuristics on solving instance can be a reference for choosing a heuristic. This paper will discuss about comparison Savings Algorithm and Nearest Neighbour on Solving Russian TSP Instances. For gene

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3

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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4

Ursani, Ziauddin, and David W. Corne. "Introducing Complexity Curtailing Techniques for the Tour Construction Heuristics for the Travelling Salesperson Problem." Journal of Optimization 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/4786268.

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In this paper, complexity curtailing techniques are introduced to create faster version of insertion heuristics, that is, cheapest insertion heuristic (CIH) and largest insertion heuristic (LIH), effectively reducing their complexities fromO(n3)toO(n2)with no significant effect on quality of solution. This paper also examines relatively not very known heuristic concept of max difference and shows that it can be culminated into a full-fledged max difference insertion heuristic (MDIH) by defining its missing steps. Further to this the paper extends the complexity curtailing techniques to MDIH to

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5

Shperberg, Shahaf, Ariel Felner, Lior Siag, and Nathan R. Sturtevant. "On the Properties of All-Pair Heuristics." Proceedings of the International Symposium on Combinatorial Search 17 (June 1, 2024): 127–33. http://dx.doi.org/10.1609/socs.v17i1.31550.

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While most work in heuristic search concentrates on goal-specific heuristics, which estimate the shortest path cost from any state to the goal, we explore all-pair heuristics that estimate distances between all pairs of states. We examine the relationship between these heuristic functions and the shortest distance function they estimate, revealing that all-pair consistent heuristics may violate the triangle inequality. Thus, we introduce a new property for heuristics called Δ-consistency, requiring adherence to the triangle inequality. Additionally, we present a method for transforming standar

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6

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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7

Özcan, Ender, Mustafa Misir, Gabriela Ochoa, and Edmund K. Burke. "A Reinforcement Learning - Great-Deluge Hyper-Heuristic for Examination Timetabling." International Journal of Applied Metaheuristic Computing 1, no. 1 (2010): 39–59. http://dx.doi.org/10.4018/jamc.2010102603.

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Hyper-heuristics can be identified as methodologies that search the space generated by a finite set of low level heuristics for solving search problems. An iterative hyper-heuristic framework can be thought of as requiring a single candidate solution and multiple perturbation low level heuristics. An initially generated complete solution goes through two successive processes (heuristic selection and move acceptance) until a set of termination criteria is satisfied. A motivating goal of hyper-heuristic research is to create automated techniques that are applicable to a wide range of problems wi

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8

Kuroiwa, Ryo, Alexander Shleyfman, Chiara Piacentini, Margarita P. Castro, and J. Christopher Beck. "LM-cut and Operator Counting Heuristics for Optimal Numeric Planning with Simple Conditions." Proceedings of the International Conference on Automated Planning and Scheduling 31 (May 17, 2021): 210–18. http://dx.doi.org/10.1609/icaps.v31i1.15964.

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We consider optimal numeric planning with numeric conditions consisting of linear expressions of numeric state variables and actions that increase or decrease numeric state variables by constant quantities. We build on previous research to introduce a new variant of the numeric hmax heuristic based on the delete-relaxed version of such planning tasks. Although our hmax heuristic is inadmissible, it yields a numeric version of the classical LM-cut heuristic which is admissible. Further, we prove that our LM-cut heuristic neither dominates nor is dominated by the existing numeric heuristic hmax(

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9

BOUZY, BRUNO. "HISTORY AND TERRITORY HEURISTICS FOR MONTE CARLO GO." New Mathematics and Natural Computation 02, no. 02 (2006): 139–46. http://dx.doi.org/10.1142/s1793005706000427.

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Recently, the Monte Carlo approach has been applied to computer go with promising success. INDIGO uses such an approach which can be enhanced with specific heuristics. This paper assesses two heuristics within the 19 × 19 Monte Carlo go framework of INDIGO: the territory heuristic and the history heuristic, both in their internal and external versions. The external territory heuristic is more effective, leading to a 40-point improvement on 19 × 19 boards. The external history heuristic brings about a 10-point improvement. The internal territory heuristic yields a few points improvement, and th

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10

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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11

Seipp, Jendrik, Florian Pommerening, and Malte Helmert. "New Optimization Functions for Potential Heuristics." Proceedings of the International Conference on Automated Planning and Scheduling 25 (April 8, 2015): 193–201. http://dx.doi.org/10.1609/icaps.v25i1.13714.

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Potential heuristics, recently introduced by Pommerening et al., characterize admissible and consistent heuristics for classical planning as a set of declarative constraints. Every feasible solution for these constraints defines an admissible heuristic, and we can obtain heuristics that optimize certain criteria such as informativeness by specifying suitable objective functions. The original paper only considered one such objective function: maximizing the heuristic value of the initial state. In this paper, we explore objectives that attempt to maximize heuristic estimates for all states (rea

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12

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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13

Drake, John H., Matthew Hyde, Khaled Ibrahim, and Ender Ozcan. "A genetic programming hyper-heuristic for the multidimensional knapsack problem." Kybernetes 43, no. 9/10 (2014): 1500–1511. http://dx.doi.org/10.1108/k-09-2013-0201.

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Purpose – Hyper-heuristics are a class of high-level search techniques which operate on a search space of heuristics rather than directly on a search space of solutions. The purpose of this paper is to investigate the suitability of using genetic programming as a hyper-heuristic methodology to generate constructive heuristics to solve the multidimensional 0-1 knapsack problem Design/methodology/approach – Early hyper-heuristics focused on selecting and applying a low-level heuristic at each stage of a search. Recent trends in hyper-heuristic research have led to a number of approaches being de

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14

Wallace, Steve, Adrian Reid, Jin-Su Kang, and Daniel Clinciu. "A Comparison of the Usability of Heuristic Evaluations for Online Help." Information Design Journal 20, no. 1 (2013): 58–68. http://dx.doi.org/10.1075/idj.20.1.05wal.

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This study compares the usability of a general heuristic evaluation to that of a domain-specific heuristic evaluation focused on technical documentation. Eight technical writers used both heuristic evaluations to identify usability problems in an online help application. The validity of the usability problems they identified was ascertained by user testing. No significant difference was found in overall effectiveness or efficiency. However, writers indicated greater satisfaction with the general heuristic evaluation, while the domain-specific heuristic evaluation was more effective in some cat

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15

Poo Hernandez, Sergio, and Vadim Bulitko. "Speeding Up Heuristic Function Synthesis via Extending the Formula Grammar." Proceedings of the International Symposium on Combinatorial Search 12, no. 1 (2021): 233–35. http://dx.doi.org/10.1609/socs.v12i1.18594.

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Heuristic search algorithms have long been used in video-game AI for unit navigation and planning. The quality of the solution they produce depends substantially on the quality of the heuristic function they use. Recent work automatically synthesized human-readable heuristic functions for a given pathfinding map. This enables tailoring a heuristic to the map but is expensive since each map requires an independent synthesis run. In this paper we propose and evaluate re-using elements of heuristics synthesized for one map in synthesizing heuristics for another map. We do so by adding parts of a

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16

Pommerening, Florian, Gabriele Röger, Malte Helmert, and Blai Bonet. "LP-Based Heuristics for Cost-Optimal Planning." Proceedings of the International Conference on Automated Planning and Scheduling 24 (May 11, 2014): 226–34. http://dx.doi.org/10.1609/icaps.v24i1.13621.

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Many heuristics for cost-optimal planning are based on linear programming. We cover several interesting heuristics of this type by a common framework that fixes the objective function of the linear program. Within the framework, constraints from different heuristics can be combined in one heuristic estimate which dominates the maximum of the component heuristics. Different heuristics of the framework can be compared on the basis of their constraints. With this new method of analysis, we show dominance of the recent LP-based state-equation heuristic over optimal cost partitioning on single-vari

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17

Narayanan, Venkatraman, Sandip Aine, and Maxim Likhachev. "Improved Multi-Heuristic A* for Searching with Uncalibrated Heuristics." Proceedings of the International Symposium on Combinatorial Search 6, no. 1 (2021): 78–86. http://dx.doi.org/10.1609/socs.v6i1.18350.

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Recently, several researchers have brought forth the benefits of searching with multiple (and possibly inadmissible) heuristics, arguing how different heuristics could be independently useful in different parts of the state space. However, algorithms that use inadmissible heuristics in the traditional best-first sense, such as the recently developed Multi-Heuristic A* (MHA*), are subject to a crippling calibration problem: they prioritize nodes for expansion by additively combining the cost-to-come and the inadmissible heuristics even if those heuristics have no connection with the cost-to-go

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18

Savolainen, Reijo. "Heuristics elements of information-seeking strategies and tactics: a conceptual analysis." Journal of Documentation 73, no. 6 (2017): 1322–42. http://dx.doi.org/10.1108/jd-11-2016-0144.

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Purpose The purpose of this paper is to elaborate the picture of strategies and tactics for information seeking and searching by focusing on the heuristic elements of such strategies and tactics. Design/methodology/approach A conceptual analysis of a sample of 31 pertinent investigations was conducted to find out how researchers have approached heuristics in the above context since the 1970s. To achieve this, the study draws on the ideas produced within the research programmes on Heuristics and Biases, and Fast and Frugal Heuristics. Findings Researchers have approached the heuristic elements

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19

Luna Gutierrez, Ricardo, and Matteo Leonetti. "Meta Reinforcement Learning for Heuristic Planing." Proceedings of the International Conference on Automated Planning and Scheduling 31 (May 17, 2021): 551–59. http://dx.doi.org/10.1609/icaps.v31i1.16003.

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Heuristic planning has a central role in classical planning applications and competitions. Thanks to this success, there has been an increasing interest in using Deep Learning to create high-quality heuristics in a supervised fashion, learning from optimal solutions of previously solved planning problems. Meta-Reinforcement learning is a fast growing research area concerned with learning, from many tasks, behaviours that can quickly generalize to new tasks from the same distribution of the training ones. We make a connection between meta-reinforcement learning and heuristic planning, showing t

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20

Moon, Seongsoo, and Mary Inaba. "Boost SAT Solver with Hybrid Branching Heuristic." Proceedings of the International Symposium on Combinatorial Search 8, no. 1 (2021): 56–63. http://dx.doi.org/10.1609/socs.v8i1.18422.

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Most state-of-the-art satisfiability (SAT) solvers are capable of solving large application instances with efficient branching heuristics. The VSIDS heuristic is widely used because of its robustness. This paper focuses on the inherent ties in VSIDS and proposes a new branching heuristic called TBVSIDS, which attempts to break the ties with the consideration of the interplay between the branching heuristic and learned clauses. However, a branching heuristic cannot cover all problems, and its performance improves when combined with an appropriate configuration. Therefore, we also propose a hybr

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21

Shanklin, Roslyn, Philip Kortum, and Claudia Ziegler Acemyan. "Adaptation of Heuristic Evaluations for the Physical Environment." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, no. 1 (2020): 1135–39. http://dx.doi.org/10.1177/1071181320641272.

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Previous work has investigated the need for domain specific heuristics. Nielsen’s ten heuristics offer a general list of principles, but those principles may not capture usability issues specific to a given interface. Studies have demonstrated methods to establish a domain specific heuristic set, but very little research has been conducted on interfaces in the physical environment, creating a gap in the state-of-the-art. The research described in this paper aims to address this gap by developing an environmental heuristic set; the heuristic set was developed specifically for the Houston light

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22

Dienes, Zoltan. "How Do I Know What My Theory Predicts?" Advances in Methods and Practices in Psychological Science 2, no. 4 (2019): 364–77. http://dx.doi.org/10.1177/2515245919876960.

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To get evidence for or against a theory relative to the null hypothesis, one needs to know what the theory predicts. The amount of evidence can then be quantified by a Bayes factor. Specifying the sizes of the effect one’s theory predicts may not come naturally, but I show some ways of thinking about the problem, some simple heuristics that are often useful when one has little relevant prior information. These heuristics include the room-to-move heuristic (for comparing mean differences), the ratio-of-scales heuristic (for regression slopes), the ratio-of-means heuristic (for regression slopes

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23

Soria-Alcaraz, Jorge A., Gabriela Ochoa, Andres Espinal, Marco A. Sotelo-Figueroa, Manuel Ornelas-Rodriguez, and Horacio Rostro-Gonzalez. "A Methodology for Classifying Search Operators as Intensification or Diversification Heuristics." Complexity 2020 (February 13, 2020): 1–10. http://dx.doi.org/10.1155/2020/2871835.

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Selection hyper-heuristics are generic search tools that dynamically choose, from a given pool, the most promising operator (low-level heuristic) to apply at each iteration of the search process. The performance of these methods depends on the quality of the heuristic pool. Two types of heuristics can be part of the pool: diversification heuristics, which help to escape from local optima, and intensification heuristics, which effectively exploit promising regions in the vicinity of good solutions. An effective search strategy needs a balance between these two strategies. However, it is not str

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24

Aine, Sandip, Siddharth Swaminathan, Venkatraman Narayanan, Victor Hwang, and Maxim Likhachev. "Multi-Heuristic A*." Proceedings of the International Symposium on Combinatorial Search 5, no. 1 (2021): 207–8. http://dx.doi.org/10.1609/socs.v5i1.18306.

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We present a novel heuristic search framework, called Multi-Heuristic A* (MHA*), that simultaneously uses multiple, arbitrarily inadmissible heuristic functions and one consistent heuristic to search for complete and bounded suboptimal solutions. This simplifies the de- sign of heuristics and enables the search to effectively combine the guiding powers of different heuristic func- tions. We support these claims with experimental results on full-body manipulation for PR2 robots.

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25

Helmert, Malte. "Landmark Heuristics for the Pancake Problem." Proceedings of the International Symposium on Combinatorial Search 1, no. 1 (2010): 109–10. http://dx.doi.org/10.1609/socs.v1i1.18176.

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We describe the gap heuristic for the pancake problem, which dramatically outperforms current abstraction-based heuristics for this problem. The gap heuristic belongs to a family of landmark heuristics that have recently been very successfully applied to planning problems.

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Domshlak, Carmel, Erez Karpas, and Shaul Markovitch. "To Max or Not to Max: Online Learning for Speeding Up Optimal Planning." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (2010): 1071–76. http://dx.doi.org/10.1609/aaai.v24i1.7741.

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It is well known that there cannot be a single "best" heuristic for optimal planning in general. One way of overcoming this is by combining admissible heuristics (e.g. by using their maximum), which requires computing numerous heuristic estimates at each state. However, there is a tradeoff between the time spent on computing these heuristic estimates for each state, and the time saved by reducing the number of expanded states. We present a novel method that reduces the cost of combining admissible heuristics for optimal search, while maintaining its benefits. Based on an idealized search space

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27

Sievers, Silvan, Martin Wehrle, Malte Helmert, and Michael Katz. "Strengthening Canonical Pattern Databases with Structural Symmetries." Proceedings of the International Symposium on Combinatorial Search 8, no. 1 (2021): 91–99. http://dx.doi.org/10.1609/socs.v8i1.18429.

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Symmetry-based state space pruning techniques have proved to greatly improve heuristic search based classical planners. Similarly, abstraction heuristics in general and pattern databases in particular are key ingredients of such planners. However, only little work has dealt with how the abstraction heuristics behave under symmetries. In this work, we investigate the symmetry properties of the popular canonical pattern databases heuristic. Exploiting structural symmetries, we strengthen the canonical pattern databases by adding symmetric pattern databases, making the resulting heuristic invaria

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Uzoma, Ihediohamma Raphael, Shelena Soosay Nathan, Nor Laily Hashim, and Hanif. "INCLUSIVITY IN MOBILE SHOPPING APPS: AN EMPHASIS ON LEARNABILITY CHECKLISTS IN CONDUCTING A HEURISTIC EVALUATION." Journal of Digital System Development 1 (October 31, 2023): 46–58. http://dx.doi.org/10.32890/jdsd2023.1.5.

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Studies on heuristic evaluation of mobile applications have been an emerging domain. However, existing checklists are too general and unable to clearly define the measurements for evaluating mobile shopping applications. This paper proposes suitable heuristics under the learnability measure for a checklist in conducting heuristic evaluation in supporting the inclusivity of a mobile shopping application. This study was conducted in two phases: generate the learnability measures based on heuristics and sub-heuristics from content analysis and verify the proposed heuristic evaluation checklist fr

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tetlock, philip e. "gauging the heuristic value of heuristics." Behavioral and Brain Sciences 28, no. 4 (2005): 562–63. http://dx.doi.org/10.1017/s0140525x05430095.

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heuristics are necessary but far from sufficient explanations for moral judgment. this commentary stresses: (a) the need to complement cold, cognitive-economizing functionalist accounts with hot, value-expressive, social-identity-affirming accounts; and (b) the importance of conducting reflective-equilibrium thought and laboratory experiments that explore the permeability of the boundaries people place on the “thinkable.”

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Drake, John H., Ender Özcan, and Edmund K. Burke. "A Case Study of Controlling Crossover in a Selection Hyper-heuristic Framework Using the Multidimensional Knapsack Problem." Evolutionary Computation 24, no. 1 (2016): 113–41. http://dx.doi.org/10.1162/evco_a_00145.

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Hyper-heuristics are high-level methodologies for solving complex problems that operate on a search space of heuristics. In a selection hyper-heuristic framework, a heuristic is chosen from an existing set of low-level heuristics and applied to the current solution to produce a new solution at each point in the search. The use of crossover low-level heuristics is possible in an increasing number of general-purpose hyper-heuristic tools such as HyFlex and Hyperion. However, little work has been undertaken to assess how best to utilise it. Since a single-point search hyper-heuristic operates on

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Özçağdavul, Mazlum. "A COMPREHENSIVE ANALYSIS OF MULTI-STRATEGY MEMETIC ALGORITHMS INCORPORATING LOW-LEVEL HEURISTICS AND ACCEPTANCE MECHANISMS." AYBU Business Journal 4, no. 1 (2024): 1–23. http://dx.doi.org/10.61725/abj.1499654.

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Hyper-heuristics are designed to be reusable, domain-independent methods for addressing complex computational issues. While there are specialized approaches that work well for particular problems, they often require parameter tuning and cannot be transferred to other problems. Memetic Algorithms combine genetic algorithms and local search techniques. The evolutionary interaction of memes allows for the creation of intelligent complexes capable of solving computational problems. Hyper-heuristics are a high-level search technique that operates on a set of low-level heuristics that directly addre

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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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Clausecker, Robert K. P., and Florian Schintke. "A Measure of Quality for IDA* Heuristics." Proceedings of the International Symposium on Combinatorial Search 12, no. 1 (2021): 55–63. http://dx.doi.org/10.1609/socs.v12i1.18551.

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We present a novel way to judge the performance of IDA* heuristics. With this measure of heuristic quality η, different heuristics for the same problem space can be compared objectively without regards to a particular problem instance. We show how η can be used to model the performance expectations of PDB heuristics. By drawing histograms of the contributions of different parts of the search space to η, we show what parts are most critical to the quality of a heuristic and contribute to the long-standing question on what h values are most critical to the performance of an IDA* heuristic.

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Fortunato, David, and Randolph T. Stevenson. "Heuristics in Context." Political Science Research and Methods 7, no. 2 (2016): 311–30. http://dx.doi.org/10.1017/psrm.2016.37.

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A growing literature in political science has pointed to the importance of heuristics in explaining citizens’ political attitudes, beliefs, and behaviors. At the same time, the multidisciplinary research on heuristics in general has revealed that individuals seem to use heuristics sensibly—applying them (perhaps subconsciously) when they are likely to be helpful but not otherwise. We extend this multidisciplinary work to political behavior and present a general theory of contextual variation in political heuristic use applied to discover under what conditions (i.e., what political contexts) vo

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Veerapaneni, Rishi, Muhammad Suhail Saleem, and Maxim Likhachev. "Learning Local Heuristics for Search-Based Navigation Planning." Proceedings of the International Conference on Automated Planning and Scheduling 33, no. 1 (2023): 634–38. http://dx.doi.org/10.1609/icaps.v33i1.27245.

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Graph search planning algorithms for navigation typically rely heavily on heuristics to efficiently plan paths. As a result, while such approaches require no training phase and can directly plan long horizon paths, they often require careful hand designing of informative heuristic functions. Recent works have started bypassing hand designed heuristics by using machine learning to learn heuristic functions that guide the search algorithm. While these methods can learn complex heuristic functions from raw input, they i) require significant training and ii) do not generalize well to new maps and

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Ursani, Ziauddin, and Ahsan Ahmad Ursani. "Augmented tour construction heuristics for the travelling salesman problem." International Journal of Industrial Optimization 4, no. 2 (2023): 131–44. http://dx.doi.org/10.12928/ijio.v4i2.7875.

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Tour construction heuristics serve as fundamental techniques in optimizing the routes of a traveling salesman. These heuristics remain significant as foundational methods for generating initial solutions to the Traveling Salesman Problem (TSP), facilitating subsequent applications of tour improvement heuristics. These heuristics effectively comprise the iterative application of city node selection and insertion. However, thus far, no attempts have been made to enhance the basic structure of tour construction heuristics to bring a better initial solution for the advanced heuristics. This study

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37

Yiu, Ying Fung, Jing Du, and Rabi Mahapatra. "Evolutionary Heuristic A* Search: Pathfinding Algorithm with Self-Designed and Optimized Heuristic Function." International Journal of Semantic Computing 13, no. 01 (2019): 5–23. http://dx.doi.org/10.1142/s1793351x19400014.

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The performance and efficiency of A* search algorithm heavily depends on the quality of the heuristic function. Therefore, designing an optimal heuristic function becomes the primary goal of developing a search algorithm for specific domains in artificial intelligence. However, it is difficult to design a well-constructed heuristic function without careful consideration and trial-and-error, especially for complex pathfinding problems. The complexity of a heuristic function increases and becomes unmanageable to design when an increasing number of parameters are involved. Existing approaches oft

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38

Cox, James L., Stephen Lucci, and Tayfun Pay. "Effects of Dynamic Variable - Value Ordering Heuristics on the Search Space of Sudoku Modeled as a Constraint Satisfaction Problem." Inteligencia Artificial 22, no. 63 (2019): 1–15. http://dx.doi.org/10.4114/intartif.vol22iss63pp1-15.

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We carry out a detailed analysis of the effects of different dynamic variable and value ordering heuristics on the search space of Sudoku when the encoding method and the filtering algorithm are fixed. Our study starts by examining lexicographical variable and value ordering and evaluates different combinations of dynamic variable and value ordering heuristics. We eventually build up to a dynamic variable ordering heuristic that has two rounds of tie-breakers, where the second tie-breaker is a dynamic value ordering heuristic. We show that our method that uses this interlinked heuristic outper

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39

Domshlak, C., E. Karpas, and S. Markovitch. "Online Speedup Learning for Optimal Planning." Journal of Artificial Intelligence Research 44 (August 21, 2012): 709–55. http://dx.doi.org/10.1613/jair.3676.

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Domain-independent planning is one of the foundational areas in the field of Artificial Intelligence. A description of a planning task consists of an initial world state, a goal, and a set of actions for modifying the world state. The objective is to find a sequence of actions, that is, a plan, that transforms the initial world state into a goal state. In optimal planning, we are interested in finding not just a plan, but one of the cheapest plans. A prominent approach to optimal planning these days is heuristic state-space search, guided by admissible heuristic functions. Numerous admissible

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40

Helmert, Malte, and Carmel Domshlak. "Landmarks, Critical Paths and Abstractions: What's the Difference Anyway?" Proceedings of the International Conference on Automated Planning and Scheduling 19 (October 16, 2009): 162–69. http://dx.doi.org/10.1609/icaps.v19i1.13370.

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Current heuristic estimators for classical domain-independent planning are usually based on one of four ideas: delete relaxations, critical paths, abstractions, and, most recently, landmarks. Previously, these different ideas for deriving heuristic functions were largely unconnected.We prove that admissible heuristics based on these ideas are in fact very closely related. Exploiting this relationship, we introduce a new admissible heuristic called the landmark cut heuristic, which compares favourably with the state of the art in terms of heuristic accuracy and overall performance.

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41

Chen, Wenlin, Yixin Chen, Kilian Weinberger, Qiang Lu, and Xiaoping Chen. "Goal-Oriented Euclidean Heuristics with Manifold Learning." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (2013): 173–79. http://dx.doi.org/10.1609/aaai.v27i1.8615.

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Recently, a Euclidean heuristic (EH) has been proposed for A* search. EH exploits manifold learning methods to construct an embedding of the state space graph, and derives an admissible heuristic distance between two states from the Euclidean distance between their respective embedded points. EH has shown good performance and memory efficiency in comparison to other existing heuristics such as differential heuristics. However, its potential has not been fully explored. In this paper, we propose a number of techniques that can significantly improve the quality of EH. We propose a goal-oriented

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42

Trevizan, Felipe, Sylvie Thiébaux, and Patrik Haslum. "Occupation Measure Heuristics for Probabilistic Planning." Proceedings of the International Conference on Automated Planning and Scheduling 27 (June 5, 2017): 306–15. http://dx.doi.org/10.1609/icaps.v27i1.13840.

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For the past 25 years, heuristic search has been used to solve domain-independent probabilistic planning problems, but with heuristics that determinise the problem and ignore precious probabilistic information. To remedy this situation, we explore the use of occupation measures, which represent the expected number of times a given action will be executed in a given state of a policy. By relaxing the well-known linear program that computes them, we derive occupation measure heuristics -- the first admissible heuristics for stochastic shortest path problems (SSPs) taking probabilities into accou

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43

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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44

Goldenberg, Meir, Ariel Felner, Nathan Sturtevant, and Jonathan Schaeffer. "Portal-Based True-Distance Heuristics for Path Finding." Proceedings of the International Symposium on Combinatorial Search 1, no. 1 (2010): 39–45. http://dx.doi.org/10.1609/socs.v1i1.18169.

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True distance memory-based heuristics (TDHs) were recently introduced as a way to obtain admissible heuristics for explicit state spaces. In this paper, we introduce a new TDH, the portal-based heuristic. The domain is partitioned into regions and portals between regions are identified. True distances between all pairs of portals are stored and used to obtain admissible heuristics throughout the search. We introduce an A*-based algorithm that takes advantage of the special properties of the new heuristic. We study the advantages and limitations of the new heuristic. Our experimental results sh

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45

Mellouli, O., I. Hafidi, and A. Metrane. "A modified choice function hyper-heuristic with Boltzmann function." Mathematical Modeling and Computing 8, no. 4 (2021): 736–46. http://dx.doi.org/10.23939/mmc2021.04.736.

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Hyper-heuristics are a subclass of high-level research methods that function in a low-level heuristic research space. Their aim objective is to improve the level of generality for solving combinatorial optimization problems using two main components: a methodology for the heuristic selection and a move acceptance criterion, to ensure intensification and diversification [1]. Thus, rather than working directly on the problem's solutions and selecting one of them to proceed to the next step at each stage, hyper-heuristics operates on a low-level heuristic research space. The choice function is on

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46

Lissovoi, Andrei, Pietro S. Oliveto, and John Alasdair Warwicker. "On the Time Complexity of Algorithm Selection Hyper-Heuristics for Multimodal Optimisation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 2322–29. http://dx.doi.org/10.1609/aaai.v33i01.33012322.

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Selection hyper-heuristics are automated algorithm selection methodologies that choose between different heuristics during the optimisation process. Recently selection hyperheuristics choosing between a collection of elitist randomised local search heuristics with different neighbourhood sizes have been shown to optimise a standard unimodal benchmark function from evolutionary computation in the optimal expected runtime achievable with the available low-level heuristics. In this paper we extend our understanding to the domain of multimodal optimisation by considering a hyper-heuristic from the

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47

Haslum, Patrik. "hm(P) = h1(Pm): Alternative Characterisations of the Generalisation From hmax To hm." Proceedings of the International Conference on Automated Planning and Scheduling 19 (October 16, 2009): 354–57. http://dx.doi.org/10.1609/icaps.v19i1.13384.

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The hm (m = 1 ...) family of admissible heuristics for STRIPS planning with additive costs generalise the hmax heuristic, which results when m = 1. We show that the step from h1 to hm can be made by changing the planning problem instead of the heuristic function. This furthers our understanding of the hm heuristic, and may inspire application of the same generalisation to admissible heuristics stronger than hmax. As an example, we show how it applies to the additive variant of hm obtained via cost splitting.

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48

Štolba, Michal, Daniel Fišer, and Antonín Komenda. "Potential Heuristics for Multi-Agent Planning." Proceedings of the International Conference on Automated Planning and Scheduling 26 (March 30, 2016): 308–16. http://dx.doi.org/10.1609/icaps.v26i1.13757.

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Distributed heuristic search is a well established technique for multi-agent planning. It has been shown that distributed heuristics may crucially improve the search guidance, but are costly in terms of communication and computation time. One solution is to compute a heuristic additively, in the sense that each agent can compute its part of the heuristic independently and obtain a complete heuristic estimate by summing up the individual parts. In this paper, we show that the recently published potential heuristic is a good candidate for such heuristic, moreover admissible. We also demonstrate

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49

Liang, Rui Shi, and Min Huang. "Heuristics for Domain-Independent Planning: A Survey." Applied Mechanics and Materials 135-136 (October 2011): 573–77. http://dx.doi.org/10.4028/www.scientific.net/amm.135-136.573.

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Increasing interest has been devoted to Planning as Heuristic Search over the years. Intense research has focused on deriving fast and accurate heuristics for domain-independent planning. This paper reports on an extensive survey and analysis of research work related to heuristic derivation techniques for state space search. Survey results reveal that heuristic techniques have been extensively applied in many efficient planners and result in impressive performances. We extend the survey analysis to suggest promising avenues for future research in heuristic derivation and heuristic search techn

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50

Zahavi, U., A. Felner, N. Burch, and R. C. Holte. "Predicting the Performance of IDA* using Conditional Distributions." Journal of Artificial Intelligence Research 37 (February 18, 2010): 41–83. http://dx.doi.org/10.1613/jair.2890.

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Korf, Reid, and Edelkamp introduced a formula to predict the number of nodes IDA* will expand on a single iteration for a given consistent heuristic, and experimentally demonstrated that it could make very accurate predictions. In this paper we show that, in addition to requiring the heuristic to be consistent, their formula's predictions are accurate only at levels of the brute-force search tree where the heuristic values obey the unconditional distribution that they defined and then used in their formula. We then propose a new formula that works well without these requirements, i.e., it can

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