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Journal articles on the topic 'Heuristic'
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1
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 (March 31, 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 generating length of route, Savings Algorithm is better than Nearest Neighbour, while for generating CPU time, Nearest Neighbour is better than Savings Algorithm. Travelling Salesman Problem (TSP) merupakan permasalahan penentuan rute terpendek yang diawali dari titik start untuk mengunjungi sekumpulan titik tepat sekali dan diakhiri dengan kembali ke titik start. Beberapa Heuristik yang cukup populer untuk menyelesaikan TSP antara lain Savings Algorithm dan Nearest Neighbour. Kemampuan Heuristik dalam menyelesaikan TSP berbeda-beda, sehingga diperlukan sebuah acuan untuk menentukan Heuristik yang akan digunakan. Membandingkan Heuristik dalam menyelesaikan instance dapat menjadi acuan untuk pemilihan Heuristik. Pada paper ini akan dibahas mengenai perbandingan Savings Algorithm dan Nearest Neighbour dalam menyelesaikan Russian TSP Instances. Untuk panjang rute yang dihasilkan, maka Savings Algorithm lebih baik dibandingkan Nearest Neighbour, sedangkan untuk CPU Time yang dihasilkan, maka Nearest Neighbour lebih baik dibandingkan Savings Algorithm.
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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, no. 1 (September 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 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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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 a heuristic will be effective for it. Our results suggest that a predictive characteristic is a heuristic's goal distance rank correlation (GDRC), a robust measure of whether it orders nodes according to distance to a goal. We demonstrate that GDRC can be used to automatically construct abstraction-based heuristics for greedy best-first search that are more effective than those built by methods oriented toward optimal search. These results reinforce the point that suboptimal search deserves sustained attention and specialized methods of its own.
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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 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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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 standard consistent heuristics to be Δ-consistent, showcasing its benefits through a synthetic example. We then show that common heuristic families inherently exhibit Δ-consistency. This positive finding encourages the use of all-pair consistent heuristics, and prompts further investigation into the optimality of A*, when given an all-pair heuristic instead of a goal-specific heuristic.
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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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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 (January 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, and J. Christopher Beck. "LM-cut and Operator Counting Heuristics for Optimal Numeric Planning with Simple Conditions." Proceedings of the International Conference on Automated Planning and Scheduling 31 (May 17, 2021): 210–18. http://dx.doi.org/10.1609/icaps.v31i1.15964.
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We consider optimal numeric planning with numeric conditions consisting of linear expressions of numeric state variables and actions that increase or decrease numeric state variables by constant quantities. We build on previous research to introduce a new variant of the numeric hmax heuristic based on the delete-relaxed version of such planning tasks. Although our hmax heuristic is inadmissible, it yields a numeric version of the classical LM-cut heuristic which is admissible. Further, we prove that our LM-cut heuristic neither dominates nor is dominated by the existing numeric heuristic hmax(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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BOUZY, BRUNO. "HISTORY AND TERRITORY HEURISTICS FOR MONTE CARLO GO." New Mathematics and Natural Computation 02, no. 02 (July 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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Wilt, Christopher, and Wheeler Ruml. "Speedy Versus Greedy Search." Proceedings of the International Symposium on Combinatorial Search 5, no. 1 (September 1, 2021): 184–92. http://dx.doi.org/10.1609/socs.v5i1.18320.
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In work on satisficing search, there has been substantial attention devoted to how to solve problems associated with local minima or plateaus in the heuristic function. One technique that has been shown to be quite promising is using an alternative heuristic function that does not estimate cost-to-go, but rather estimates distance-to-go. Empirical results generally favor using the distance-to-go heuristic over the cost-to-go heuristic, but there is currently little beyond intuition to explain the difference. We begin by empirically showing that the success of the distance-to-go heuristic 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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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 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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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 (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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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 (November 3, 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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Wallace, Steve, Adrian Reid, Jin-Su Kang, and Daniel Clinciu. "A Comparison of the Usability of Heuristic Evaluations for Online Help." Information Design Journal 20, no. 1 (September 23, 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, and Vadim Bulitko. "Speeding Up Heuristic Function Synthesis via Extending the Formula Grammar." Proceedings of the International Symposium on Combinatorial Search 12, no. 1 (July 21, 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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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-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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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 (September 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 (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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Savolainen, Reijo. "Heuristics elements of information-seeking strategies and tactics: a conceptual analysis." Journal of Documentation 73, no. 6 (October 9, 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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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 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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Moon, Seongsoo, and Mary Inaba. "Boost SAT Solver with Hybrid Branching Heuristic." Proceedings of the International Symposium on Combinatorial Search 8, no. 1 (September 1, 2021): 56–63. http://dx.doi.org/10.1609/socs.v8i1.18422.
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Most state-of-the-art satisfiability (SAT) solvers are capable of solving large application instances with efficient branching heuristics. The VSIDS heuristic is widely used because of its robustness. This paper focuses on the inherent ties in VSIDS and proposes a new branching heuristic called TBVSIDS, which attempts to break the ties with the consideration of the interplay between the branching heuristic and learned clauses. However, a branching heuristic cannot cover all problems, and its performance improves when combined with an appropriate configuration. Therefore, we also propose a 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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Aine, Sandip, Siddharth Swaminathan, Venkatraman Narayanan, Victor Hwang, and Maxim Likhachev. "Multi-Heuristic A*." Proceedings of the International Symposium on Combinatorial Search 5, no. 1 (September 1, 2021): 207–8. http://dx.doi.org/10.1609/socs.v5i1.18306.
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We present a novel heuristic search framework, called Multi-Heuristic A* (MHA*), that simultaneously uses multiple, arbitrarily inadmissible heuristic functions and one consistent heuristic to search for complete and bounded suboptimal solutions. This simplifies the de- sign of heuristics and enables the search to effectively combine the guiding powers of different heuristic func- tions. We support these claims with experimental results on full-body manipulation for PR2 robots.
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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 (December 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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Dienes, Zoltan. "How Do I Know What My Theory Predicts?" Advances in Methods and Practices in Psychological Science 2, no. 4 (November 14, 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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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 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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tetlock, philip e. "gauging the heuristic value of heuristics." Behavioral and Brain Sciences 28, no. 4 (August 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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Domshlak, Carmel, Erez Karpas, and Shaul Markovitch. "To Max or Not to Max: Online Learning for Speeding Up Optimal Planning." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 4, 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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Helmert, Malte. "Landmark Heuristics for the Pancake Problem." Proceedings of the International Symposium on Combinatorial Search 1, no. 1 (August 25, 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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Sievers, Silvan, Martin Wehrle, Malte Helmert, and Michael Katz. "Strengthening Canonical Pattern Databases with Structural Symmetries." Proceedings of the International Symposium on Combinatorial Search 8, no. 1 (September 1, 2021): 91–99. http://dx.doi.org/10.1609/socs.v8i1.18429.
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Symmetry-based state space pruning techniques have proved to greatly improve heuristic search based classical planners. Similarly, abstraction heuristics in general and pattern databases in particular are key ingredients of such planners. However, only little work has dealt with how the abstraction heuristics behave under symmetries. In this work, we investigate the symmetry properties of the popular canonical pattern databases heuristic. Exploiting structural symmetries, we strengthen the canonical pattern databases by adding symmetric pattern databases, making the resulting heuristic 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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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 from the learnability measures through expert reviews. As a result, a verified Learnability checklist consisting of three heuristics was selected: recognition rather than recall, user control and freedom and match between the system and the real world. For each heuristic, suitable sub-heuristics and evaluation questions were identified. Through the learnability checklist, usability experts and analysts can consistently perform their evaluation during heuristics evaluation on other mobile shopping apps. This is to ensure that the apps have all relevant features that enhance the learning ability of their users, using the apps confidently and proficiently, providing their contentment in every usage scenario.
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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 (March 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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Özçağdavul, Mazlum. "A COMPREHENSIVE ANALYSIS OF MULTI-STRATEGY MEMETIC ALGORITHMS INCORPORATING LOW-LEVEL HEURISTICS AND ACCEPTANCE MECHANISMS." AYBU Business Journal 4, no. 1 (June 30, 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 address the solution. They have two main components: heuristic selection and move acceptance mechanisms. The heuristic selection method determines which low-level heuristic to use, while the move acceptance mechanism decides whether to accept or reject the resulting solution. In this study, we explore a multi-meme memetic algorithm as a hyper-heuristic that integrates and manages multiple hyper-heuristics (Modified Choice Function All Moves, Reinforcement Learning with Great Deluge, and Simple Random Only Improvement) and parameters of heuristics (such as mutation rates and search depth). We conducted an empirical study testing two different variations of the proposed hyper-heuristic. The first algorithm uses the Only Improvement acceptance technique for both Reinforcement Learning and Simple Random, and All Moves for Modified Choice Function. In the second version, the Great Deluge method replaces Only Improvement for Reinforcement Learning. The second algorithm's results were the best of all competitors from the CHeSC2011 competition, achieving the fourth-best hyper-heuristic performance.
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Fišer, Daniel, Álvaro Torralba, and Jörg Hoffmann. "Operator-Potential Heuristics for Symbolic Search." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 9 (June 28, 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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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 (July 21, 2021): 55–63. http://dx.doi.org/10.1609/socs.v12i1.18551.
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We present a novel way to judge the performance of IDA* heuristics. With this measure of heuristic quality η, different heuristics for the same problem space can be compared objectively without regards to a particular problem instance. We show how η can be used to model the performance expectations of PDB heuristics. By drawing histograms of the contributions of different parts of the search space to η, we show what parts are most critical to the quality of a heuristic and contribute to the long-standing question on what h values are most critical to the performance of an IDA* heuristic.
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Fortunato, David, and Randolph T. Stevenson. "Heuristics in Context." Political Science Research and Methods 7, no. 2 (October 17, 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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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 (July 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 longer horizon paths. Our contribution is showing that instead of learning a global heuristic estimate, we can define and learn local heuristics which results in a significantly smaller learning problem and improves generalization. We show that using such local heuristics can reduce node expansions by 2-20x while maintaining bounded suboptimality, are easy to train, and generalize to new maps & long horizon plans.
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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 (September 11, 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 aims to enhance tour construction heuristics without compromising their theoretical complexity. Specifically, an iterative step of partial tour deconstruction has been introduced to the existing heuristics. This additional step has been implemented and evaluated with three highly performing tour construction heuristics: the farthest insertion heuristic, the max difference insertion heuristic, and the fast max difference insertion heuristic. The results demonstrate that augmenting these heuristics with the partial tour deconstruction step improves the best, worst, and average solutions while preserving their theoretical complexity
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Yiu, Ying Fung, Jing Du, and Rabi Mahapatra. "Evolutionary Heuristic A* Search: Pathfinding Algorithm with Self-Designed and Optimized Heuristic Function." International Journal of Semantic Computing 13, no. 01 (March 2019): 5–23. http://dx.doi.org/10.1142/s1793351x19400014.
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The performance and efficiency of A* search algorithm heavily depends on the quality of the heuristic function. Therefore, designing an optimal heuristic function becomes the primary goal of developing a search algorithm for specific domains in artificial intelligence. However, it is difficult to design a well-constructed heuristic function without careful consideration and trial-and-error, especially for complex pathfinding problems. The complexity of a heuristic function increases and becomes unmanageable to design when an increasing number of parameters are involved. Existing approaches often avoid complex heuristic function design: they either trade-off the accuracy for faster computation or taking advantage of the parallelism for better performance. The objective of this paper is to reduce the difficulty of complex heuristic function design for A* search algorithm. We aim to design an algorithm that can be automatically optimized to achieve rapid search with high accuracy and low computational cost. In this paper, we present a novel design and optimization method for a Multi-Weighted-Heuristics function (MWH) named Evolutionary Heuristic A* search (EHA*) to: (1) minimize the effort on heuristic function design via Genetic Algorithm (GA), (2) optimize the performance of A* search and its variants including but not limited to WA* and MHA*, and (3) guarantee the completeness and optimality. EHA* algorithm enables high performance searches and significantly simplifies the processing of heuristic design. We apply EHA* to multiple grid-based pathfinding benchmarks to evaluate the performance. Our experiment result shows that EHA* (1) is capable of choosing an accurate heuristic function that provides an optimal solution, (2) can identify and eliminate inefficient heuristics, (3) is able to automatically design multi-heuristics function, and (4) minimizes both the time and space complexity.
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Cox, James L., Stephen Lucci, and Tayfun Pay. "Effects of Dynamic Variable - Value Ordering Heuristics on the Search Space of Sudoku Modeled as a Constraint Satisfaction Problem." Inteligencia Artificial 22, no. 63 (January 10, 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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Domshlak, C., E. Karpas, and S. Markovitch. "Online Speedup Learning for Optimal Planning." Journal of Artificial Intelligence Research 44 (August 21, 2012): 709–55. http://dx.doi.org/10.1613/jair.3676.
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Domain-independent planning is one of the foundational areas in the field of Artificial Intelligence. A description of a planning task consists of an initial world state, a goal, and a set of actions for modifying the world state. The objective is to find a sequence of actions, that is, a plan, that transforms the initial world state into a goal state. In optimal planning, we are interested in finding not just a plan, but one of the cheapest plans. A prominent approach to optimal planning these days is heuristic state-space search, guided by admissible heuristic functions. Numerous admissible 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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Chen, Wenlin, Yixin Chen, Kilian Weinberger, Qiang Lu, and Xiaoping Chen. "Goal-Oriented Euclidean Heuristics with Manifold Learning." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (June 30, 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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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 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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Helmert, Malte, and Carmel Domshlak. "Landmarks, Critical Paths and Abstractions: What's the Difference Anyway?" Proceedings of the International Conference on Automated Planning and Scheduling 19 (October 16, 2009): 162–69. http://dx.doi.org/10.1609/icaps.v19i1.13370.
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Current heuristic estimators for classical domain-independent planning are usually based on one of four ideas: delete relaxations, critical paths, abstractions, and, most recently, landmarks. Previously, these different ideas for deriving heuristic functions were largely unconnected.We prove that admissible heuristics based on these ideas are in fact very closely related. Exploiting this relationship, we introduce a new admissible heuristic called the landmark cut heuristic, which compares favourably with the state of the art in terms of heuristic accuracy and overall performance.
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Speck, David, Florian Geißer, and Robert Mattmüller. "When Perfect Is Not Good Enough: On the Search Behaviour of Symbolic Heuristic Search." Proceedings of the International Conference on Automated Planning and Scheduling 30 (June 1, 2020): 263–71. http://dx.doi.org/10.1609/icaps.v30i1.6670.
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Symbolic search has proven to be a competitive approach to cost-optimal planning, as it compactly represents sets of states by symbolic data structures. While heuristics for symbolic search exist, symbolic bidirectional blind search empirically outperforms its heuristic counterpart and is therefore the dominant search strategy. This prompts the question of why heuristics do not seem to pay off in symbolic search. As a first step in answering this question, we investigate the search behaviour of symbolic heuristic search by means of BDDA⋆. Previous work identified the partitioning of state sets according to their heuristic values as the main bottleneck. We theoretically and empirically evaluate the search behaviour of BDDA⋆ and reveal another fundamental problem: we prove that the use of a heuristic does not always improve the search performance of BDDA⋆. In general, even the perfect heuristic can exponentially deteriorate search performance.
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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 (August 25, 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, 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 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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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 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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Haslum, Patrik. "hm(P) = h1(Pm): Alternative Characterisations of the Generalisation From hmax To hm." Proceedings of the International Conference on Automated Planning and Scheduling 19 (October 16, 2009): 354–57. http://dx.doi.org/10.1609/icaps.v19i1.13384.
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The hm (m = 1 ...) family of admissible heuristics for STRIPS planning with additive costs generalise the hmax heuristic, which results when m = 1. We show that the step from h1 to hm can be made by changing the planning problem instead of the heuristic function. This furthers our understanding of the hm heuristic, and may inspire application of the same generalisation to admissible heuristics stronger than hmax. As an example, we show how it applies to the additive variant of hm obtained via cost splitting.
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Štolba, Michal, Daniel Fišer, and Antonín Komenda. "Potential Heuristics for Multi-Agent Planning." Proceedings of the International Conference on Automated Planning and Scheduling 26 (March 30, 2016): 308–16. http://dx.doi.org/10.1609/icaps.v26i1.13757.
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Distributed heuristic search is a well established technique for multi-agent planning. It has been shown that distributed heuristics may crucially improve the search guidance, but are costly in terms of communication and computation time. One solution is to compute a heuristic additively, in the sense that each agent can compute its part of the heuristic independently and obtain a complete heuristic estimate by summing up the individual parts. In this paper, we show that the recently published potential heuristic is a good candidate for such heuristic, moreover admissible. We also demonstrate how the multi-agent distributed A* search can be modified in order to benefit from such additive heuristic. The modified search equipped with a distributed potential heuristic outperforms the state of the art.
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Liang, Rui Shi, and Min Huang. "Heuristics for Domain-Independent Planning: A Survey." Applied Mechanics and Materials 135-136 (October 2011): 573–77. http://dx.doi.org/10.4028/www.scientific.net/amm.135-136.573.
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Increasing interest has been devoted to Planning as Heuristic Search over the years. Intense research has focused on deriving fast and accurate heuristics for domain-independent planning. This paper reports on an extensive survey and analysis of research work related to heuristic derivation techniques for state space search. Survey results reveal that heuristic techniques have been extensively applied in many efficient planners and result in impressive performances. We extend the survey analysis to suggest promising avenues for future research in heuristic derivation and heuristic search techniques.
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Zahavi, U., A. Felner, N. Burch, and R. C. Holte. "Predicting the Performance of IDA* using Conditional Distributions." Journal of Artificial Intelligence Research 37 (February 18, 2010): 41–83. http://dx.doi.org/10.1613/jair.2890.
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Korf, Reid, and Edelkamp introduced a formula to predict the number of nodes IDA* will expand on a single iteration for a given consistent heuristic, and experimentally demonstrated that it could make very accurate predictions. In this paper we show that, in addition to requiring the heuristic to be consistent, their formula's predictions are accurate only at levels of the brute-force search tree where the heuristic values obey the unconditional distribution that they defined and then used in their formula. We then propose a new formula that works well without these requirements, i.e., it can make accurate predictions of IDA*'s performance for inconsistent heuristics and if the heuristic values in any level do not obey the unconditional distribution. In order to achieve this we introduce the conditional distribution of heuristic values which is a generalization of their unconditional heuristic distribution. We also provide extensions of our formula that handle individual start states and the augmentation of IDA* with bidirectional pathmax (BPMX), a technique for propagating heuristic values when inconsistent heuristics are used. Experimental results demonstrate the accuracy of our new method and all its variations.