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Burgers, Jan y Rik Hoekstra. "De Digitale Charterbank Nederland". BMGN - Low Countries Historical Review 136, n.º 3 (30 de septiembre de 2021): 92–119. http://dx.doi.org/10.51769/bmgn-lchr.6997.
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In de Nederlandse archieven worden tienduizenden oorkonden (ofwel charters) bewaard uit de middeleeuwen en de vroegmoderne tijd. Dit materiaal vormt een onschatbare bron van informatie over allerlei maatschappelijke aspecten. Toch worden deze oorkonden in het historisch onderzoek nog weinig gebruikt, vanwege de gecompliceerde heuristiek: de documenten zijn verspreid over tientallen archieven en honderden archieffondsen. De Digitale Charterbank Nederland (DCN) maakt het grootste deel van dit corpus nu toegankelijk in een geïntegreerde database, waarin alle stukken vindbaar zijn. Ons artikel bespreekt de opzet van DCN en de praktische consequenties daarvan voor de gebruiker. De database kent specifieke mogelijkheden maar ook bepaalde beperkingen, en dit artikel toont hoe DCN kan helpen bij zowel gedetailleerd onderzoek naar personen of plaatsen als bij brede studies over een lange periode. Tevens wordt ingegaan op de invloed van de nieuwe grootschalige digitale bronontsluitingen en de bijbehorende hulpmiddelen en technieken op het historisch onderzoek.The Dutch archives hold tens of thousands of charters from the Middle Ages and early modern period, providing an invaluable source of information on various societal aspects. Yet, this material is scarcely used in historical research mainly due to its complicated heuristics: the documents are spread across dozens of archives and hundreds of archival funds. The Digitale Charterbank Nederland (DCN) now makes most of this corpus accessible through an integrated database in which all documents can be found. Our article discusses the set-up of DCN and its practical consequences for the user. The database has specific possibilities but also certain limitations, and this article shows how DCN can help with both detailed research into persons or places, and with more broadly oriented research covering a long period of time. The article further includes a reflection on the impact of new large scale digital source retrieval systems and the associated tools and techniques on historical research.
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Lazuarni, Shafiera, William Wendy Ary, Try Wulandari y Emilda Emilda. "Apakah Investor Milenial Penyebab Terbentuknya Teknikal Anomali di Pasar Modal Indonesia?" MBIA 21, n.º 2 (28 de agosto de 2022): 125–39. http://dx.doi.org/10.33557/mbia.v21i2.1747.
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This study aims to examine the role of millennial investors' simplification behavior (heuristic) on the formation of technical anomalies in the capital market and the role of these anomalies on investment performance. The method of the data analysis used the SEM (Structural Equation Modeling) method with the WarpPLS analysis tool. The Variance Accounted For (VAF) method was added in this study as an additional test to explore whether technical anomalies can mediate the effect of heuristics on investment performance. The results proved that the heuristic behavior (availability, representativeness, and hindsight) of millennial investors is one of the factors causing technical anomalies in the capital market, except for the availability heuristic. The limitations of this study were the first measurement items/items used were minimal, amounting to two to four items for each construct. Second, this study could not detail how millennial investors behaved when selling or buying shares. This study is essential as investors can understand their investment behavior and they can make better decisions. Further, providing information regarding the behavior of millennial investors to the government because the millennial generation is an essential key in encouraging them to be at the forefront of the Indonesian economy. The implication is that the government can develop strategies and policies to increase the involvement of the millennial generation in investing in the capital market.
Keywords: Investor Behavior, Millennial Investors, Anomalies, Heuristics, Capital Market
Abstrak
Penelitian ini bertujuan menguji peran perilaku penyederhanaan (heuristik) investor milenial terhadap terbentuknya teknikal anomali di pasar modal dan peran anomali tersebut terhadap kinerja investasi. Metode analisis data menggunakan metode SEM (Structural Equation Modeling) dengan alat analisis WarpPLS. Metode Variance Accounted For (VAF) juga ditambahkan dalam penelitian ini sebagai ujian tambahan untuk mengeksplorasi apakah teknikal anomali mampu memediasi pengaruh antara heuristik terhadap kinerja investasi. Hasil penelitian menunjukkan bahwa perilaku heuristik (availability, representativeness, dan hindsight) investor milenial terbukti menjadi salah satu faktor penyebab terjadinya teknikal anomali di pasar modal, kecuali availability heuristik. Keterbatasan penelitian ini pertama item pengukuran/item yang digunakan sangat terbatas berjumlah dua hingga empat item untuk masing-masing konstruk. Kedua, penelitian ini tidak mampu menangkap secara detail bagaimana perilaku investor milenial secara terpisah saat menjual atau membeli saham. Penelitian ini penting dilakukan agar investor dapat memahami perilaku investasi mereka sehingga mampu mengambil keputusan lebih baik. Kedua, memberikan informasi terkait perilaku investor milenial ke pemerintahan karena generasi milenial menjadi kunci penting dalam mendorong mereka menjadi garda terdepan dalam perekonomian Indonesia. Implikasinya pemerintah dapat menyusun strategi dan kebijakan dalam peningkatan keterlibatan generasi milenial dalam berinvestasi di pasar modal.
Kata Kunci: Perilaku Investor, Investor Milenial, Anomali, Heuristik, Pasar Modal
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Seipp, Jendrik. "Better Orders for Saturated Cost Partitioning in Optimal Classical Planning". Proceedings of the International Symposium on Combinatorial Search 8, n.º 1 (1 de septiembre de 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 the three saturated cost partitioning heuristics computed separately for each heuristic type. Our new algorithm for choosing the orders in which saturated cost partitioning considers the heuristics allows us to compute heuristics outperforming not only the maximizing heuristic but even state-of-the-art planners.
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Sihotang, Emil Hasudungan, Muhamad Adam, Marlina Widiyanti y Yuliani Yuliani. "PENGARUH PERILAKU KEUANGAN HEURISTIK TERHADAP KEPUTUSAN INVESTASI SAHAM PADA BURSA EFEK INDONESIA DENGAN INVESTASI ETIS SEBAGAI VARIABEL INTERVENING STUDI EMPIRIS PADA MASYARAKAT INVESTOR KOTA PALEMBANG". Jurnal Riset Ekonomi dan Bisnis 13, n.º 1 (21 de abril de 2020): 28. http://dx.doi.org/10.26623/jreb.v13i1.2108.
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<p class="BasicParagraph">Tujuan dari penelitian ini untuk mengetahui bukti empiris pengaruh perilaku keuangan heuristik (<em>salience, rerpresentativness dan mental accounting</em>) dan investasi etis terhadap keputusan investasi saham yang dilakukan oleh investor individu di kota Palembang pada tahun 2019. Penelitian ini bersifat kausal menggunakan data primer 145 responden dengan teknik purposive sampling. Program analisis Structural Equation Modeling (SEM) Amos digunakan untuk mengukur hubungan antara variabel penelitian. Hasil penelitian menunjukkan bahwa perilaku keuangan heuristik salience dan mental accounting berpengaruh signifikan terhadap keputusan investasi, sedangkan <em>representativness</em> tidak signifikan terhadap keputusan investasi dan investasi etis mampu memediasi perilaku keuangan heuristik terhadap keputusan investasi.</p><p class="BasicParagraph"><strong><em>The purpose of this study is to find out empirical evidence of the influence of financial heuristics (salience, rerpresentativness and mental accounting) and ethical investment on investment decisions made by individual investors in the city of Palembang in 2019. This study uses causal data using primary data of 145 respondents with taking techniques purposive sample. Analysis of the Amos Structural Equation Modeling (SEM) program is used to measure the relationship between research variables. The results showed heuristic financial facts salience and mental accounting have a significant effect on investment decisions, while representation does not have a significant effect on investment returns and ethical investment is able to mediate heuristic financial problems on investment decisions.</em></strong></p>
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Drake, John H., Matthew Hyde, Khaled Ibrahim y Ender Ozcan. "A genetic programming hyper-heuristic for the multidimensional knapsack problem". Kybernetes 43, n.º 9/10 (3 de noviembre de 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 developed to automatically generate new heuristics from a set of heuristic components. A population of heuristics to rank knapsack items are trained on a subset of test problems and then applied to unseen instances. Findings – The results over a set of standard benchmarks show that genetic programming can be used to generate constructive heuristics which yield human-competitive results. Originality/value – In this work the authors show that genetic programming is suitable as a method to generate reusable constructive heuristics for the multidimensional 0-1 knapsack problem. This is classified as a hyper-heuristic approach as it operates on a search space of heuristics rather than a search space of solutions. To our knowledge, this is the first time in the literature a GP hyper-heuristic has been used to solve the multidimensional 0-1 knapsack problem. The results suggest that using GP to evolve ranking mechanisms merits further future research effort.
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Seipp, Jendrik, Florian Pommerening y Malte Helmert. "New Optimization Functions for Potential Heuristics". Proceedings of the International Conference on Automated Planning and Scheduling 25 (8 de abril de 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 (reachable and unreachable), maximize heuristic estimates for a sample of reachable states, maximize the number of detected dead ends, or minimize search effort. We also search for multiple heuristics with complementary strengths that can be combined to obtain even better heuristics.
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Ursani, Ziauddin y 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 create its faster version. The resultant heuristic, that is, fast max difference insertion heuristic (FMDIH), outperforms the “farthest insertion” heuristic (FIH) across a wide spectrum of popular datasets with statistical significance, even though both the heuristics have the same worst case complexity ofO(n2). It should be noted that FIH is considered best among lowest order complexity heuristics. The complexity curtailing techniques presented here open up the new area of research for their possible extension to other heuristics.
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Narayanan, Venkatraman, Sandip Aine y Maxim Likhachev. "Improved Multi-Heuristic A* for Searching with Uncalibrated Heuristics". Proceedings of the International Symposium on Combinatorial Search 6, n.º 1 (1 de septiembre de 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 (e.g., the heuristics are uncalibrated) . For instance, if the inadmissible heuristic were an order of magnitude greater than the perfect heuristic, an algorithm like MHA* would simply reduce to a weighted A* search with one consistent heuristic. In this work, we introduce a general multi-heuristic search framework that solves the calibration problem and as a result a) facilitates the effective use of multiple uncalibrated inadmissible heuristics, and b) provides significantly better performance than MHA* whenever tighter sub-optimality bounds on solution quality are desired. Experimental evaluations on a complex full-body robotics motion planning problem and large sliding tile puzzles demonstrate the benefits of our framework.
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Pommerening, Florian, Gabriele Röger, Malte Helmert y Blai Bonet. "LP-Based Heuristics for Cost-Optimal Planning". Proceedings of the International Conference on Automated Planning and Scheduling 24 (11 de mayo de 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-variable abstractions. We also show that the previously suggested extension of the state-equation heuristic to exploit safe variables cannot lead to an improved heuristic estimate. We experimentally evaluate the potential of the proposed framework on an extensive suite of benchmark tasks.
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Shanklin, Roslyn, Philip Kortum y Claudia Ziegler Acemyan. "Adaptation of Heuristic Evaluations for the Physical Environment". Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, n.º 1 (diciembre de 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 rail system, METRORail. Following development, the heuristic set was validated against Nielsen’s more general heuristics through several field studies. Results highlighted that there were significantly more usability issues identified when using the environment-based heuristics than the general heuristics. This suggests that domain specific heuristics provide a framework that allows evaluators to capture usability issues particular to the interface of the physical environment.
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Aritonang, Bichar y Adi Sutopo. "PENGARUH MODEL PEMBELAJARAN LOGAN AVENUE PROBLEM SOLVING (LAPS)-HEURISTIK TERHADAP KEMAMPUAN KOGNITIF DAN SOFT SKILL". JEVTE: Journal of Electrical Vocational Teacher Education 2, n.º 1 (23 de junio de 2022): 10. http://dx.doi.org/10.24114/jevte.v2i1.35880.
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Abstract This study aims to determine the differences in student learning outcomes of class XI TITL on the subject of Electrical Motor Installation by using the Logan Avenue Problem Solving (Laps)-Heuristic and Expository learning model. This research belongs to the type of quasi-experimental research. The population in this study were all students of class XI TITL SMK Negeri 1 Percut Sei Tuan, totaling 60 students. Sampling used a random sampling technique, with class XI TITL 1 as a group of 30 students in the Logan Avenue Problem Solving (Laps)-Heuristic learning model and class XI TITL 2 as a group of 30 students in the Expository learning method. The results showed that: there was a significant difference in the Logan Avenue Problem Solving (Laps)-Heuristic and Expository learning model on student learning outcomes with the subject of electric motor installation at SMK Negeri 1 Percut Sei Tuan. This is evidenced by the average cognitive learning outcomes of Logan Avenue Problem Solving (Laps)-Heuristics class students of 86.15 and an average soft skill of 85.7 while in the Expository class the cognitive learning outcomes are 78.65 and the average score is 78.65. soft skills of 80.9. The results of the statistical t-test with a significant level (α) = 0.05 obtained t-count learning outcomes of 4.071 and t-table = 2.002 so that tcount > ttable. The statistical results of the soft skills t-test with a significant level (α) = 0.05 obtained tcount 2,683 and ttable 2,002 so that tcount > ttable. The Logan Avenue Problem Solving (Laps)-Heuristic learning model is higher when taught than the Expository learning method on cognitive abilities and soft skills with electric motor installation subjects. Keywords: Cognitive Ability, Soft Skill Ability, Logan Avenue Problem Solving (Laps)-Heuristic, ExpositoryAbstrakPenelitian ini bertujuan untuk mengetahui perbedaan hasil belajar siswa kelas XI TITL pada mata pelajaran Instalasi Motor Listrik dengan menggunakan model pembelajaran Logan Avenue Problem Solving (Laps)-Heuristik dan Ekspositori. Penelitian ini termasuk jenis penelitian quasi experiment. Populasi dalam penelitian ini adalah seluruh siswa kelas XI TITL SMK Negeri 1 Percut Sei Tuan yang berjumlah 60 siswa. Pengambilan sampel digunakan teknik sampel random sampling, dengan kelas XI TITL 1 sebagai kelompok model pembelajaran Logan Avenue Problem Solving (Laps)-Heuristik yang berjumlah 30 orang siswa dan kelas XI TITL 2 sebagai kelompok metode pembelajaran Ekspositori yang berjumlah 30 orang siswa. Hasil penelitian menunjukkan bahwa: ada perbedaan yang signifikan model pembelajaran Logan Avenue Problem Solving (Laps)-Heuristik dan Ekspositori terhadap hasil belajar siswa dengan mata pelajaran instalasi motor listrik SMK Negeri 1 Percut Sei Tuan. Hal ini dibuktikan dangan rata-rata hasil belajar kognitif siswa kelas Logan Avenue Problem Solving (Laps)-Heuristik sebesar 86,15 dan rata-rata soft skill 85,7 sedangkan pada kelas Ekspositori hasil belajar kognitif sebesar 78,65 dan rata-rata nilai soft skill sebesar 80,9. Hasil statistik uji-t hitung dengan taraf signifikan (α) = 0,05 diperoleh thitung hasil belajar sebesar 4,071 dan ttabel = 2,002 sehingga thitung > ttabel. Hasil statistik uji-t soft skill dengan taraf signifikan (α) = 0,05 diperoleh thitung 2,683 dan ttabel 2,002 sehingga thitung > ttabel. Model pembelajaran Logan Avenue Problem Solving (Laps)-Heuristik lebih tinggi bila diajarkan dibanding dengan metode pembelajaran Ekspositori terhadap kemampuan kognitif dan soft skill dengan mata pelajaran instalasi motor listrik. Kata Kunci : Kemampuan Kognitif, Kemampuan Soft Skill, Logan Avenue Problem Solving (Laps)-Heuristik, Ekspositori
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BOUZY, BRUNO. "HISTORY AND TERRITORY HEURISTICS FOR MONTE CARLO GO". New Mathematics and Natural Computation 02, n.º 02 (julio de 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 the internal history heuristic has already been assessed on 19 × 19 boards in previous publications. Most of these heuristics were used by INDIGO at the 2004 Computer Olympiad.
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Özcan, Ender, Mustafa Misir, Gabriela Ochoa y Edmund K. Burke. "A Reinforcement Learning - Great-Deluge Hyper-Heuristic for Examination Timetabling". International Journal of Applied Metaheuristic Computing 1, n.º 1 (enero de 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 with different characteristics. Some previous studies show that different combinations of heuristic selection and move acceptance as hyper-heuristic components might yield different performances. This study investigates whether learning heuristic selection can improve the performance of a great deluge based hyper-heuristic using an examination timetabling problem as a case study.
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Kuroiwa, Ryo, Alexander Shleyfman, Chiara Piacentini, Margarita P. Castro y 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 (17 de mayo de 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(hbd). We show that admissibility also holds when integrating the numeric cuts into the operator-counting (OC) heuristic producing an admissible numeric version of the OC heuristic. Through experiments, we demonstrate that both these heuristics compete favorably with the state-of-the-art heuristics: in particular, while sometimes expanding more nodes than other heuristics, numeric OC solves 19 more problem instances than the next closest heuristic.
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tetlock, philip e. "gauging the heuristic value of heuristics". Behavioral and Brain Sciences 28, n.º 4 (agosto de 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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Speck, David, André Biedenkapp, Frank Hutter, Robert Mattmüller y Marius Lindauer. "Learning Heuristic Selection with Dynamic Algorithm Configuration". Proceedings of the International Conference on Automated Planning and Scheduling 31 (17 de mayo de 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 ignore the internal search dynamics of a planning system, which can help to select the most useful heuristics for the current expansion step. We show that dynamic algorithm configuration can be used for dynamic heuristic selection which takes into account the internal search dynamics of a planning system. Furthermore, we prove that this approach generalizes over existing approaches and that it can exponentially improve the performance of the heuristic search. To learn dynamic heuristic selection, we propose an approach based on reinforcement learning and show empirically that domain-wise learned policies, which take the internal search dynamics of a planning system into account, can exceed existing approaches.
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Savolainen, Reijo. "Heuristics elements of information-seeking strategies and tactics: a conceptual analysis". Journal of Documentation 73, n.º 6 (9 de octubre de 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 in three major ways. First, these elements are defined as general level constituents of browsing strategies in particular. Second, heuristics are approached as search tips. Third, there are examples of conceptualizations of individual heuristics. Familiarity heuristic suggests that people tend to prefer sources that have worked well in similar situations in the past. Recognition heuristic draws on an all-or-none distinction of the information objects, based on cues such as information scent. Finally, representativeness heuristic is based on recalling similar instances of events or objects and judging their typicality in terms of genres, for example. Research limitations/implications As the study focuses on three heuristics only, the findings cannot be generalized to describe the use of all heuristic elements of strategies and tactics for information seeking and searching. Originality/value The study pioneers by providing an in-depth analysis of the ways in which the heuristic elements are conceptualized in the context of information seeking and searching. The findings contribute to the elaboration of the conceptual issues of information behavior research.
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Soria-Alcaraz, Jorge A., Gabriela Ochoa, Andres Espinal, Marco A. Sotelo-Figueroa, Manuel Ornelas-Rodriguez y Horacio Rostro-Gonzalez. "A Methodology for Classifying Search Operators as Intensification or Diversification Heuristics". Complexity 2020 (13 de febrero de 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 straightforward to categorize an operator as intensification or diversification heuristic on complex domains. Therefore, we propose an automated methodology to do this classification. This brings methodological rigor to the configuration of an iterated local search hyper-heuristic featuring diversification and intensification stages. The methodology considers the empirical ranking of the heuristics based on an estimation of their capacity to either diversify or intensify the search. We incorporate the proposed approach into a state-of-the-art hyper-heuristic solving two domains: course timetabling and vehicle routing. Our results indicate improved performance, including new best-known solutions for the course timetabling problem.
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Wilt, Christopher y Wheeler Ruml. "Speedy Versus Greedy Search". Proceedings of the International Symposium on Combinatorial Search 5, n.º 1 (1 de septiembre de 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 appears related to its having smaller local minima. We then discuss a reasonable theoretical model of heuristics and show that, under this model, the expected size of local minima is higher for a cost- to-go heuristic than a distance-to-go heuristic, offering a possible explanation as to why distance-to-go heuristics tend to outperform cost-to-go heuristics.
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Cichowicz, T., M. Drozdowski, M. Frankiewicz, G. Pawlak, F. Rytwinski y J. Wasilewski. "Hyper-heuristics for cross-domain search". Bulletin of the Polish Academy of Sciences: Technical Sciences 60, n.º 4 (1 de diciembre de 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 hyper-heuristic methodologies are discussed.
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Helmert, Malte. "Landmark Heuristics for the Pancake Problem". Proceedings of the International Symposium on Combinatorial Search 1, n.º 1 (25 de agosto de 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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Trevizan, Felipe, Sylvie Thiébaux y Patrik Haslum. "Occupation Measure Heuristics for Probabilistic Planning". Proceedings of the International Conference on Automated Planning and Scheduling 27 (5 de junio de 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 account. We show that these heuristics can also be obtained by extending recent operator-counting heuristic formulations used in deterministic planning. Since the heuristics are formulated as linear programs over occupation measures, they can easily be extended to more complex probabilistic planning models, such as constrained SSPs (C-SSPs). Moreover, their formulation can be tightly integrated into i-dual, a recent LP-based heuristic search algorithm for (constrained) SSPs, resulting in a novel probabilistic planning approach in which policy update and heuristic computation work in unison. Our experiments in several domains demonstrate the benefits of these new heuristics and approach.
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Luna Gutierrez, Ricardo y Matteo Leonetti. "Meta Reinforcement Learning for Heuristic Planing". Proceedings of the International Conference on Automated Planning and Scheduling 31 (17 de mayo de 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 that heuristic functions meta-learned from planning problems, in a given domain, can outperform both popular domain-independent heuristics, and heuristics learned by supervised learning. Furthermore, while most supervised learning algorithms rely on ad-hoc encodings of the state representation, our method uses as input a general PDDL 3.1 description. We evaluated our heuristic with an A* planner on six domains from the International Planning Competition and the FF Domain Collection, showing that the meta-learned heuristic leads to the expansion, on average, of fewer states than three popular heuristics used by the FastDownward planner, and a supervised-learned heuristic.
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Greco, Matias, Pablo Araneda y Jorge A. Baier. "Focal Discrepancy Search for Learned Heuristics (Extended Abstract)". Proceedings of the International Symposium on Combinatorial Search 15, n.º 1 (17 de julio de 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 heuristic, to sort the focal list. In our empirical evaluation, we evaluate FS and FDS using DeepCubeA, an effective learned heuristic for the 15-puzzle. We show that FDS substantially outperforms FS. This suggests that in some domains, when a highly accurate heuristics is available, one should always consider using discrepancies for better search.
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Moon, Seongsoo y Mary Inaba. "Boost SAT Solver with Hybrid Branching Heuristic". Proceedings of the International Symposium on Combinatorial Search 8, n.º 1 (1 de septiembre de 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 hybrid model of branching heuristics based on random forest. The efficiencies of TBVSIDS and hybrid branching heuristics are evaluated on benchmarks in SAT Competitions. By constructing a model that reduces the overfitting problem, we hope to realize a hybrid branching heuristic that is widely applicable to other solvers.
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Clausecker, Robert K. P. y Florian Schintke. "A Measure of Quality for IDA* Heuristics". Proceedings of the International Symposium on Combinatorial Search 12, n.º 1 (21 de julio de 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 y Randolph T. Stevenson. "Heuristics in Context". Political Science Research and Methods 7, n.º 2 (17 de octubre de 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) voters will use a partisanship heuristic to infer the legislative votes of their legislators in imperfectly disciplined voting contexts. More specifically, we predict that US constituents of loyal partisan senators will use the partisanship heuristic more often than constituents of less loyal senators. Our empirical analysis reveals strong support for our theory, contributing to our understanding of political heuristics in general and adding nuance to our understanding of the partisanship heuristic in particular.
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Seipp, Jendrik y Malte Helmert. "Subset-Saturated Cost Partitioning for Optimal Classical Planning". Proceedings of the International Conference on Automated Planning and Scheduling 29 (25 de mayo de 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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Lissovoi, Andrei, Pietro S. Oliveto y John Alasdair Warwicker. "On the Time Complexity of Algorithm Selection Hyper-Heuristics for Multimodal Optimisation". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julio de 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 literature that can switch between elitist and nonelitist heuristics during the run. We first identify the range of parameters that allow the hyper-heuristic to hillclimb efficiently and prove that it can optimise a standard hillclimbing benchmark function in the best expected asymptotic time achievable by unbiased mutation-based randomised search heuristics. Afterwards, we use standard multimodal benchmark functions to highlight function characteristics where the hyper-heuristic is efficient by swiftly escaping local optima and ones where it is not. For a function class called CLIFFd where a new gradient of increasing fitness can be identified after escaping local optima, the hyper-heuristic is extremely efficient while a wide range of established elitist and non-elitist algorithms are not, including the well-studied Metropolis algorithm. We complete the picture with an analysis of another standard benchmark function called JUMPd as an example to highlight problem characteristics where the hyper-heuristic is inefficient. Yet, it still outperforms the wellestablished non-elitist Metropolis algorithm.
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Drake, John H., Ender Özcan y Edmund K. Burke. "A Case Study of Controlling Crossover in a Selection Hyper-heuristic Framework Using the Multidimensional Knapsack Problem". Evolutionary Computation 24, n.º 1 (marzo de 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 a single candidate solution, and two candidate solutions are required for crossover, a mechanism is required to control the choice of the other solution. The frameworks we propose maintain a list of potential solutions for use in crossover. We investigate the use of such lists at two conceptual levels. First, crossover is controlled at the hyper-heuristic level where no problem-specific information is required. Second, it is controlled at the problem domain level where problem-specific information is used to produce good-quality solutions to use in crossover. A number of selection hyper-heuristics are compared using these frameworks over three benchmark libraries with varying properties for an NP-hard optimisation problem: the multidimensional 0-1 knapsack problem. It is shown that allowing crossover to be managed at the domain level outperforms managing crossover at the hyper-heuristic level in this problem domain.
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Aliferis, C. F. y R. A. Miller. "On the Heuristic Nature of Medical Decision-Support Systems". Methods of Information in Medicine 34, n.º 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 domains, and propose a unifying definition of heuristics that encompasses formal and ad hoc systems. System developers should be aware of the heuristic nature of all problem solving done in complex real world domains, and characterize their own use of heuristics in describing system development and implementation.
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Wallace, Steve, Adrian Reid, Jin-Su Kang y Daniel Clinciu. "A Comparison of the Usability of Heuristic Evaluations for Online Help". Information Design Journal 20, n.º 1 (23 de septiembre de 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 categories and showed greater inter-rater agreement. Results suggest that differences in effectiveness were related to the level of detail of the heuristics. This study therefore recommends the incorporation of more detailed heuristics into heuristic evaluations.
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Poo Hernandez, Sergio y Vadim Bulitko. "Speeding Up Heuristic Function Synthesis via Extending the Formula Grammar". Proceedings of the International Symposium on Combinatorial Search 12, n.º 1 (21 de julio de 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 synthesized heuristic back to the grammar that defines the space of heuristic functions for the synthesis.
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Fišer, Daniel, Álvaro Torralba y Jörg Hoffmann. "Operator-Potential Heuristics for Symbolic Search". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 9 (28 de junio de 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 heuristic functions, the change of heuristic value induced by each operator can be precomputed. This facilitates their smooth integration into symbolic search. Our experiments show that this can pay off significantly: we establish a new state of the art in optimal symbolic planning.
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Goldenberg, Meir, Ariel Felner, Nathan Sturtevant y Jonathan Schaeffer. "Portal-Based True-Distance Heuristics for Path Finding". Proceedings of the International Symposium on Combinatorial Search 1, n.º 1 (25 de agosto de 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 show large performance improvements over previously-reported TDHs for commonly used classes of maps.
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Mellouli, O., I. Hafidi y A. Metrane. "A modified choice function hyper-heuristic with Boltzmann function". Mathematical Modeling and Computing 8, n.º 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 one of the hyper-heuristics that have proven their efficiency in solving combinatorial optimization problems [2–4]. At each iteration, the selection of heuristics is dependent on a score calculated by combining three different measures to guarantee both intensification and diversification for the heuristics choice process. The heuristic with the highest score is therefore chosen to be applied to the problem. Therefore, the key to the success of the choice function is to choose the correct weight parameters of its three measures. In this study, we make a state of the art in hyper-heuristic research and propose a new method that automatically controls these weight parameters based on the Boltzmann function. The results obtained from its application on five problem domains are compared with those of the standard, modified choice function proposed by Drake et al. [2,3].
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Domshlak, Carmel, Erez Karpas y Shaul Markovitch. "To Max or Not to Max: Online Learning for Speeding Up Optimal Planning". Proceedings of the AAAI Conference on Artificial Intelligence 24, n.º 1 (4 de julio de 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 model, we formulate a decision rule for choosing the best heuristic to compute at each state. We then present an active online learning approach for that decision rule, and employ the learned model to decide which heuristic to compute at each state. We evaluate this technique empirically, and show that it substantially outperforms each of the individual heuristics that were used, as well as their regular maximum.
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Jiang, He, Junying Qiu y Jifeng Xuan. "A Hyper-Heuristic Using GRASP with Path-Relinking". Journal of Information Technology Research 4, n.º 2 (abril de 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 problem as a case study. The authors use an existing simulated annealing based hyper-heuristic as a baseline. The experimental results indicate that HyGrasPr can achieve better solutions than SAHH within the same running time and the path-relinking phase is effective for the framework of HyGrasPr.
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Cox, James L., Stephen Lucci y Tayfun Pay. "Effects of Dynamic Variable - Value Ordering Heuristics on the Search Space of Sudoku Modeled as a Constraint Satisfaction Problem". Inteligencia Artificial 22, n.º 63 (10 de enero de 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 outperforms the previously studied ones with the same experimental setup. Overall, we conclude that constructing insightful dynamic variable ordering heuristics that also utilize a dynamic value ordering heuristic in their decision making process could drastically improve the search effort for some constraint satisfaction problems.
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Drexler, Dominik, Jendrik Seipp y David Speck. "Subset-Saturated Transition Cost Partitioning". Proceedings of the International Conference on Automated Planning and Scheduling 31 (17 de mayo de 2021): 131–39. http://dx.doi.org/10.1609/icaps.v31i1.15955.
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Cost partitioning admissibly combines the information from multiple heuristics for optimal state-space search. One of the strongest cost partitioning algorithms is saturated cost partitioning. It considers the heuristics in sequence and assigns to each heuristic the minimal fraction of the remaining costs that are needed for preserving all heuristic estimates. Saturated cost partitioning has recently been generalized in two directions: first, by allowing to use different costs for the transitions induced by the same operator, and second, by preserving the heuristic estimates for only a subset of states. In this work, we unify these two generalizations and show that the resulting subset-saturated transition cost partitioning algorithm usually yields stronger heuristics than the two generalizations by themselves.
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Domshlak, C., E. Karpas y S. Markovitch. "Online Speedup Learning for Optimal Planning". Journal of Artificial Intelligence Research 44 (21 de agosto de 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 heuristics have been developed, each with its own strengths and weaknesses, and it is well known that there is no single "best'' heuristic for optimal planning in general. Thus, which heuristic to choose for a given planning task is a difficult question. This difficulty can be avoided by combining several heuristics, but that requires computing numerous heuristic estimates at each state, and the tradeoff between the time spent doing so and the time saved by the combined advantages of the different heuristics might be high. We present a novel method that reduces the cost of combining admissible heuristics for optimal planning, while maintaining its benefits. Using an idealized search space model, we formulate a decision rule for choosing the best heuristic to compute at each state. We then present an active online learning approach for learning a classifier with that decision rule as the target concept, and employ the learned classifier to decide which heuristic to compute at each state. We evaluate this technique empirically, and show that it substantially outperforms the standard method for combining several heuristics via their pointwise maximum.
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Dienes, Zoltan. "How Do I Know What My Theory Predicts?" Advances in Methods and Practices in Psychological Science 2, n.º 4 (14 de noviembre de 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), the basic-effect heuristic (for analysis of variance effects), and the total-effect heuristic (for mediation analysis).
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Correa, Augusto B. y Florian Pommerening. "An Empirical Study of Perfect Potential Heuristics". Proceedings of the International Conference on Automated Planning and Scheduling 29 (25 de mayo de 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 number of features. We aim to identify the practical trade-offs between size, complexity and time for the quality of potential heuristics. Our results show that, even for simple planning tasks, finding perfect potential heuristics might be harder than expected.
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Štolba, Michal y Antonín Komenda. "Relaxation Heuristics for Multiagent Planning". Proceedings of the International Conference on Automated Planning and Scheduling 24 (11 de mayo de 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 estimate using only a projection of the problem for a single agent. In this paper, we place such approach in the context of techniques which allow sharing more information among the agents and thus improve the heuristic estimates. We thoroughly experimentally evaluate properties of our distribution of additive, max and Fast-Forward relaxation heuristics in a planner based on distributed Best-First Search. The best performing distributed relaxation heuristics favorably compares to a state-of-the-art MA-Strips planner in terms of benchmark problem coverage. Finally, we analyze impact of limited agent interactions by means of recursion depth of the heuristic estimates.
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Erkol, Şirag y Gönenç Yücel. "Influence maximization based on partial network structure information: A comparative analysis on seed selection heuristics". International Journal of Modern Physics C 28, n.º 10 (octubre de 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 the case in most of the times. For the study, a simulation model is constructed, which is capable of creating networks, performing seed selection heuristics, and simulating diffusion models. Novel metric-based seed selection heuristics that rely only on partial information are proposed and tested using the simulation model. These heuristics use local information available from nodes in the synthetically created networks. The performances of heuristics are comparatively analyzed on three different network types. The results clearly show that the performance of a heuristic depends on the structure of a network. A heuristic to be used should be selected after investigating the properties of the network at hand. More importantly, the approach of partial information provided promising results. In certain cases, selection heuristics that rely only on partial network information perform very close to similar heuristics that require complete network data.
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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 (16 de octubre de 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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Chen, Wenlin, Yixin Chen, Kilian Weinberger, Qiang Lu y Xiaoping Chen. "Goal-Oriented Euclidean Heuristics with Manifold Learning". Proceedings of the AAAI Conference on Artificial Intelligence 27, n.º 1 (30 de junio de 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 manifold learning scheme that optimizes the Euclidean distance to goals in the embedding while maintaining admissibility and consistency. We also propose a state heuristic enhancement technique to reduce the gap between heuristic and true distances. The enhanced heuristic is admissible but no longer consistent. We then employ a modified search algorithm, known as B' algorithm, that achieves optimality with inconsistent heuristics using consistency check and propagation. We demonstrate the effectiveness of the above techniques and report un-matched reduction in search costs across several non-trivial benchmark search problems.
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E. Nugraheni, Cecilia, Luciana Abednego y Maria Widyarini. "A Combination of Palmer Algorithm and Gupta Algorithm for Scheduling Problem in Apparel Industry". International Journal of Fuzzy Logic Systems 11, n.º 1 (31 de enero de 2021): 1–12. http://dx.doi.org/10.5121/ijfls.2021.11101.
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The apparel industry is a class of textile industry. Generally, the production scheduling problem in the apparel industry belongs to Flow Shop Scheduling Problems (FSSP). There are many algorithms/techniques/heuristics for solving FSSP. Two of them are the Palmer Algorithm and the Gupta Algorithm. Hyper-heuristic is a class of heuristics that enables to combine of some heuristics to produce a new heuristic. GPHH is a hyper-heuristic that is based on genetic programming that is proposed to solve FSSP [1]. This paper presents the development of a computer program that implements the GPHH. Some experiments have been conducted for measuring the performance of GPHH. From the experimental results, GPHH has shown a better performance than the Palmer Algorithm and Gupta Algorithm.
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Sievers, Silvan, Martin Wehrle, Malte Helmert y Michael Katz. "Strengthening Canonical Pattern Databases with Structural Symmetries". Proceedings of the International Symposium on Combinatorial Search 8, n.º 1 (1 de septiembre de 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 invariant under structural symmetry, thus making it especially attractive for symmetry-based pruning search methods. Further, we prove that this heuristic is at least as informative as using symmetric lookups over the original heuristic. An experimental evaluation confirms these theoretical results.
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Romanuke, Vadim. "A Hyper-Heuristic for the Preemptive Single Machine Scheduling Problem to Minimize the Total Weighted Tardiness". Applied Computer Systems 27, n.º 1 (1 de junio de 2022): 1–12. http://dx.doi.org/10.2478/acss-2022-0001.
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Abstract A problem of minimizing the total weighted tardiness in the preemptive single machine scheduling for discrete manufacturing is considered. A hyper-heuristic is presented, which is composed of 24 various heuristics, to find an approximately optimal schedule whenever finding the exact solution is practically intractable. The three heuristics are based on the well-known rules, whereas the 21 heuristics are introduced first. Therefore, the hyper-heuristic selects the best heuristic schedule among 24 schedule versions, whose total weighted tardiness is minimal. Each of the 24 heuristics can solely produce a schedule which is the best one for a given scheduling problem. Despite the percentage of zero gap instances decreases as the greater number of jobs is scheduled, the average and maximal gaps decrease as well. In particular, the percentage is not less than 80 % when up to 10 jobs are scheduled. The average gap calculated over nonzero gaps does not exceed 4 % in the case of scheduling 7 jobs. When manufacturing consists of hundreds of jobs, the hyper-heuristic is made an online scheduling algorithm by applying it only to a starting part of the manufacturing process.