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Journal articles on the topic 'Heuristics'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 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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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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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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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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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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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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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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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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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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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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Raikhert, Kostiantyn. "THE PHILOSOPHICAL HEURISTICS OF J. HARTMAN AND YA. HNATIUK." Doxa, no. 2(38) (December 18, 2022): 38–51. http://dx.doi.org/10.18524/2410-2601.2022.2(38).283062.

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The study surveys of philosophical heuristics and centers on the philosophical heuristics of J. Hartman and Ya. Hnatiuk. J. Hartman’s philosophical heuristics studies philosophy in terms of its heuristic nature. J. Hartman implies that the entire philosophy is a set of heuristic projects: from conceptions to methods of philosophy. Even philosophical heuristics is one such project. Philosophical heuristics is a metaphilosophy, the specific distinction of which is to study the heuristic nature of philosophy. Philosophical heuristics of Ya. Hnatiuk is a methodology focused on the creativity of ph

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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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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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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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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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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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Greco, Matias, Pablo Araneda, and Jorge A. Baier. "Focal Discrepancy Search for Learned Heuristics (Extended Abstract)." Proceedings of the International Symposium on Combinatorial Search 15, no. 1 (2022): 282–84. http://dx.doi.org/10.1609/socs.v15i1.21786.

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Machine learning allows learning accurate but inadmissible heuristics for hard combinatorial puzzles like the 15-puzzle, the 24-puzzle, and Rubik's cube. In this paper, we investigate how to exploit these learned heuristics in the context of heuristic search with suboptimality guarantees. Specifically, we study how Focal Search (FS), a well-known bounded-suboptimal search algorithm can be modified to better exploit inadmissible learned heuristics. We propose to use Focal Discrepancy Search (FDS) in the context of learned heuristics, which uses a discrepancy function, instead of the learned heu

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Correa, Augusto B., and Florian Pommerening. "An Empirical Study of Perfect Potential Heuristics." Proceedings of the International Conference on Automated Planning and Scheduling 29 (May 25, 2021): 114–18. http://dx.doi.org/10.1609/icaps.v29i1.3466.

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Potential heuristics are weighted functions over state features of a planning task. A recent study defines the complexity of a task as the minimum required feature complexity for a potential heuristic that makes a search backtrack-free. This gives an indication of how complex potential heuristics need to be to achieve good results in satisficing planning. However, these results do not directly transfer to optimal planning.In this paper, we empirically study how complex potential heuristics must be to represent the perfect heuristic and how close to perfect heuristics can get with a limited num

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Ozdowska, Anne, Penny Sweetser, and Mahsuum Daiiani. "A Scoping Review of Heuristics in Videos Games Research: Definitions, Development, Application, and Operationalisation." Proceedings of the ACM on Human-Computer Interaction 7, CHI PLAY (2023): 402–24. http://dx.doi.org/10.1145/3611035.

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Heuristics present a cheap and effective way of evaluating usability. However, in video games, evaluating unique player experiences that are dependent on individual preferences and abilities presents a challenge that goes beyond usability. Video games are more than just functional software, so games heuristics have been adapted to help examine functionality and experience. This paper reports on how papers published in the ACM Digital Library between 2012 and 2022 develop and apply heuristics in video games research. We found that heuristics are often used outside their intended purpose of bein

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Abdul-Razaq, Tariq, Hanan Chachan, and Faez Ali. "Modified Heuristics for Scheduling in Flow Shop to Minimize Makespan." Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 ), no. 2 (October 19, 2021): 1–20. http://dx.doi.org/10.55562/jrucs.v30i2.361.

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The NP-completeness of flow shops scheduling problems has been discussed for many years. Hence many heuristics have been proposed to obtain solutions of good quality with a small computational effort. The CDS (Campbell et al) and NEH (Nawaz, Enscore and Ham) heuristics are efficient among meta-heuristics such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA).This paper discusses some methods and suggests new developing to the methods of the scheduling in flow shop to minimize makespan problems. Our main object in this paper, from one side, is to improve efficient heuristics which

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Парфентьєва І. П. та Матвійчук К. О. "ЕТИМОЛОГІЯ ПОНЯТТЯ “ЕВРИСТИЧНИЙ ПІДХІД”". World Science 3, № 8(36) (2018): 46–49. http://dx.doi.org/10.31435/rsglobal_ws/30082018/6078.

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   The article deals with the term "heuristics", which in scientific knowledge has the designation as a "method of finding." The author conducts an analysis of scientific literature on the interpretation of heuristics as a scientific concept. Heuristics as an independent science has not been fully formed yet. Despite the large number of scientific papers devoted to questions of heuristics, they usually relate to its particular problems and do not give a clear idea of either the object or subject of heuristics or its status among other sciences. It is suggested that pedagogica

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Erkol, Şirag, and Gönenç Yücel. "Influence maximization based on partial network structure information: A comparative analysis on seed selection heuristics." International Journal of Modern Physics C 28, no. 10 (2017): 1750122. http://dx.doi.org/10.1142/s0129183117501224.

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In this study, the problem of seed selection is investigated. This problem is mainly treated as an optimization problem, which is proved to be NP-hard. There are several heuristic approaches in the literature which mostly use algorithmic heuristics. These approaches mainly focus on the trade-off between computational complexity and accuracy. Although the accuracy of algorithmic heuristics are high, they also have high computational complexity. Furthermore, in the literature, it is generally assumed that complete information on the structure and features of a network is available, which is not

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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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Seipp, Jendrik, and Malte Helmert. "Subset-Saturated Cost Partitioning for Optimal Classical Planning." Proceedings of the International Conference on Automated Planning and Scheduling 29 (May 25, 2021): 391–400. http://dx.doi.org/10.1609/icaps.v29i1.3503.

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Cost partitioning is a method for admissibly adding multiple heuristics for state-space search. Saturated cost partitioning considers the given heuristics in sequence, assigning to each heuristic the minimum fraction of remaining costs that it needs to preserve its estimates for all states. We generalize saturated cost partitioning by allowing to preserve the heuristic values of only a subset of states and show that this often leads to stronger heuristics.

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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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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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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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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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Štolba, Michal, and Antonín Komenda. "Relaxation Heuristics for Multiagent Planning." Proceedings of the International Conference on Automated Planning and Scheduling 24 (May 11, 2014): 298–306. http://dx.doi.org/10.1609/icaps.v24i1.13642.

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Similarly to classical planning, in MA-Strips multiagent planning, heuristics significantly improve efficiency of search-based planners. Heuristics based on solving a relaxation of the original planning problem are intensively studied and well understood. In particular, frequently used is the delete relaxation, where all delete effects of actions are omitted. In this paper, we present a unified view on distribution of delete relaxation heuristics for multiagent planning. Until recently, the most common approach to adaptation of heuristics for multiagent planning was to compute the heuristic es

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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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Aliferis, C. F., and R. A. Miller. "On the Heuristic Nature of Medical Decision-Support Systems." Methods of Information in Medicine 34, no. 01/02 (1995): 5–14. http://dx.doi.org/10.1055/s-0038-1634584.

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Abstract:In the realm of medical decision-support systems, the term “heuristic systems” is often considered to be synonymous with “medical artificial intelligence systems” or with “systems employing informal model(s) of problem solving”. Such a view may be inaccurate and possibly impede the conceptual development of future systems. This article examines the nature of heuristics and the levels at which heuristic solutions are introduced during system design and implementation. The authors discuss why heuristics are ubiquitous in all medical decision-support systems operating at non-trivial doma

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Sievers, Silvan, Daniel Gnad, and Álvaro Torralba. "Additive Pattern Databases for Decoupled Search." Proceedings of the International Symposium on Combinatorial Search 15, no. 1 (2022): 180–89. http://dx.doi.org/10.1609/socs.v15i1.21766.

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Abstraction heuristics are the state of the art in optimal classical planning as heuristic search. Despite their success for explicit-state search, though, abstraction heuristics are not available for decoupled state-space search, an orthogonal reduction technique that can lead to exponential savings by decomposing planning tasks. In this paper, we show how to compute pattern database (PDB) heuristics for decoupled states. The main challenge lies in how to additively employ multiple patterns, which is crucial for strong search guidance of the heuristics. We show that in the general case, for a

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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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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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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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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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Jiang, He, Junying Qiu, and Jifeng Xuan. "A Hyper-Heuristic Using GRASP with Path-Relinking." Journal of Information Technology Research 4, no. 2 (2011): 31–42. http://dx.doi.org/10.4018/jitr.2011040103.

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The goal of hyper-heuristics is to design and choose heuristics to solve complex problems. The primary motivation behind the hyper-heuristics is to generalize the solving ability of the heuristics. In this paper, the authors propose a Hyper-heuristic using GRASP with Path-Relinking (HyGrasPr). HyGrasPr generates heuristic sequences to produce solutions within an iterative procedure. The procedure of HyGrasPr consists of three phases, namely the construction phase, the local search phase, and the path-relinking phase. To show the performance of the HyGrasPr, the authors use the nurse rostering

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Messa, Frederico, and André Grahl Pereira. "A Best-First Search Algorithm for FOND Planning and Heuristic Functions to Optimize Decompressed Solution Size." Proceedings of the International Conference on Automated Planning and Scheduling 33, no. 1 (2023): 277–85. http://dx.doi.org/10.1609/icaps.v33i1.27205.

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In this work, we study fully-observable non-deterministic (FOND) planning, which models uncertainty through actions with non-deterministic effects. We present a best-first heuristic search algorithm called AND* that searches the policy-space of the FOND task to find a solution policy. We generalize the concepts of optimality, admissibility, and goal-awareness for FOND. Using these new concepts, we formalize the concept of heuristic functions that can guide a policy-space search. We analyze different aspects of the general structure of FOND solutions to introduce and characterize a set of FOND

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Lohmüller, Valentin, Daniel Schmaderer, and Christian Wolff. "A Heuristic Checklist for Second Screen Applications." i-com 18, no. 1 (2019): 55–65. http://dx.doi.org/10.1515/icom-2019-0003.

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Abstract This paper presents domain-specific heuristics for second screen applications and the development of a heuristics checklist to enable a more intuitive and structured application of the created heuristics. The heuristics presented were developed on the basis of Nielsen [12] Ten Usability Heuristics in a research-based approach using specific literature and a focus group. In order to evaluate the quality of the derived checklist, a heuristic evaluation of a second screen application with five users was carried out and its results compared to a user study with 20 participants. This resul

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Rott, Benjamin. "Rethinking heuristics – characterizations and vignettes." Lumat: International Journal of Math, Science and Technology Education 3, no. 1 (2015): 122–26. http://dx.doi.org/10.31129/lumat.v3i1.1055.

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The concept of “heuristics” or “heuristic strategies” is central to (mathematical) problem solving and related research; however, there is no generally accepted definition of this term. Trying to clarify the concept might help avoiding misunderstandings and difficulties in dealing with studies that use different terms meaning the same concepts or that use the same terms meaning different concepts. Therefore, the research presented in this paper aims at a clarification of the term “heuristics” and suggestions for the use of it in future research. Building on previous work from last year’s ProMa

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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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Klößner, Thorsten, and Jörg Hoffmann. "Pattern Databases for Stochastic Shortest Path Problems." Proceedings of the International Symposium on Combinatorial Search 12, no. 1 (2021): 131–35. http://dx.doi.org/10.1609/socs.v12i1.18561.

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Stochastic shortest-path problems (SSP) are an important subclass of MDPs for which heuristic search algorithms exist since over a decade. Yet most known heuristic functions rely on determinization so do not actually take the transition probabilities into account. The only exceptions are Trevizan et al.'s heuristics hpom and hroc, which are geared at solving more complex (constrained) MDPs. Here we contribute pattern database (PDB) heuristics for SSPs, including an additivity criterion. These new heuristics turn out to be very competitive, even when using a simple systematic generation of patt

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Quiñones, Daniela, Cristian Rusu, Diego Arancibia, Sebastián González, and María Josée Saavedra. "SNUXH: A Set of Social Network User Experience Heuristics." Applied Sciences 10, no. 18 (2020): 6547. http://dx.doi.org/10.3390/app10186547.

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With the growth and overcrowding of the internet, the use of online social networks has been increasing. Currently, social networks are used by a wide variety of users–with different objectives and in different contexts of use–, so it is essential to design intuitive and easy to use social network applications that generate a positive user experience (UX). The heuristic evaluation is a well-known evaluation method that allows detecting usability problems; a group of experts evaluates a product and/or system using a set of heuristics as a guide. Although the heuristic evaluation is oriented to

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Katz, Michael, and Carmel Domshlak. "Structural-Pattern Databases." Proceedings of the International Conference on Automated Planning and Scheduling 19 (October 16, 2009): 186–93. http://dx.doi.org/10.1609/icaps.v19i1.13351.

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Explicit abstraction heuristics, notably pattern-database and merge-and-shrink heuristics, are employed by some state-of-the-art optimal heuristic-search planners. The major limitation of these abstraction heuristics is that the size of the abstract space has to be bounded by a (large) constant. Targeting this issue, Katz and Domshlak (2008b) introduced structural, and in particular fork-decomposition, abstractions, in which the planning task is abstracted by an instance of a tractable fragment of optimal planning. At first view, however, the lunch was not free. Some of the power of the explic

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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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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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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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Seipp, Jendrik, and Malte Helmert. "Diverse and Additive Cartesian Abstraction Heuristics." Proceedings of the International Conference on Automated Planning and Scheduling 24 (May 11, 2014): 289–97. http://dx.doi.org/10.1609/icaps.v24i1.13639.

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We have recently shown how counterexample-guided abstraction refinement can be used to derive informative Cartesian abstraction heuristics for optimal classical planning. In this work we introduce two methods for producing diverse sets of heuristics within this framework, one based on goal facts, the other based on landmarks. In order to sum the heuristic estimates admissibly we present a novel way of finding cost partitionings for explicitly represented abstraction heuristics. We show that the resulting heuristics outperform other state-of-the-art abstraction heuristics on many benchmark doma

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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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