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Journal articles on the topic 'MCTS Tree Search Simulation'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 February 2022

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1

CHASLOT, GUILLAUME M. J.-B., MARK H. M. WINANDS, H. JAAP VAN DEN HERIK, JOS W. H. M. UITERWIJK, and BRUNO BOUZY. "PROGRESSIVE STRATEGIES FOR MONTE-CARLO TREE SEARCH." New Mathematics and Natural Computation 04, no. 03 (2008): 343–57. http://dx.doi.org/10.1142/s1793005708001094.

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Monte-Carlo Tree Search (MCTS) is a new best-first search guided by the results of Monte-Carlo simulations. In this article, we introduce two progressive strategies for MCTS, called progressive bias and progressive unpruning. They enable the use of relatively time-expensive heuristic knowledge without speed reduction. Progressive bias directs the search according to heuristic knowledge. Progressive unpruning first reduces the branching factor, and then increases it gradually again. Experiments assess that the two progressive strategies significantly improve the level of our Go program Mango. M

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Best, Graeme, Oliver M. Cliff, Timothy Patten, Ramgopal R. Mettu, and Robert Fitch. "Dec-MCTS: Decentralized planning for multi-robot active perception." International Journal of Robotics Research 38, no. 2-3 (2018): 316–37. http://dx.doi.org/10.1177/0278364918755924.

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We propose a decentralized variant of Monte Carlo tree search (MCTS) that is suitable for a variety of tasks in multi-robot active perception. Our algorithm allows each robot to optimize its own actions by maintaining a probability distribution over plans in the joint-action space. Robots periodically communicate a compressed form of their search trees, which are used to update the joint distribution using a distributed optimization approach inspired by variational methods. Our method admits any objective function defined over robot action sequences, assumes intermittent communication, is anyt

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Chaudhry, Muhammad Umar, Muhammad Yasir, Muhammad Nabeel Asghar, and Jee-Hyong Lee. "Monte Carlo Tree Search-Based Recursive Algorithm for Feature Selection in High-Dimensional Datasets." Entropy 22, no. 10 (2020): 1093. http://dx.doi.org/10.3390/e22101093.

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The complexity and high dimensionality are the inherent concerns of big data. The role of feature selection has gained prime importance to cope with the issue by reducing dimensionality of datasets. The compromise between the maximum classification accuracy and the minimum dimensions is as yet an unsolved puzzle. Recently, Monte Carlo Tree Search (MCTS)-based techniques have been invented that have attained great success in feature selection by constructing a binary feature selection tree and efficiently focusing on the most valuable features in the features space. However, one challenging pro

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Guo, Jian, Yaoyao Shi, Zhen Chen, Tao Yu, Bijan Shirinzadeh, and Pan Zhao. "Improved SP-MCTS-Based Scheduling for Multi-Constraint Hybrid Flow Shop." Applied Sciences 10, no. 18 (2020): 6220. http://dx.doi.org/10.3390/app10186220.

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As a typical non-deterministic polynomial (NP)-hard combinatorial optimization problem, the hybrid flow shop scheduling problem (HFSSP) is known to be a very common layout in real-life manufacturing scenarios. Even though many metaheuristic approaches have been presented for the HFSSP with makespan criterion, there are limitations of the metaheuristic method in accuracy, efficiency, and adaptability. To address this challenge, an improved SP-MCTS (single-player Monte-Carlo tree search)-based scheduling is proposed for the hybrid flow shop to minimize the makespan considering the multi-constrai

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Fu, Michael C. "Simulation-Based Algorithms for Markov Decision Processes: Monte Carlo Tree Search from AlphaGo to AlphaZero." Asia-Pacific Journal of Operational Research 36, no. 06 (2019): 1940009. http://dx.doi.org/10.1142/s0217595919400098.

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AlphaGo and its successors AlphaGo Zero and AlphaZero made international headlines with their incredible successes in game playing, which have been touted as further evidence of the immense potential of artificial intelligence, and in particular, machine learning. AlphaGo defeated the reigning human world champion Go player Lee Sedol 4 games to 1, in March 2016 in Seoul, Korea, an achievement that surpassed previous computer game-playing program milestones by IBM’s Deep Blue in chess and by IBM’s Watson in the U.S. TV game show Jeopardy. AlphaGo then followed this up by defeating the world’s n

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Lee, Gwangho, Gun Hyuk Jang, Ho Young Kang, and Giltae Song. "Predicting aptamer sequences that interact with target proteins using an aptamer-protein interaction classifier and a Monte Carlo tree search approach." PLOS ONE 16, no. 6 (2021): e0253760. http://dx.doi.org/10.1371/journal.pone.0253760.

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Oligonucleotide-based aptamers, which have a three-dimensional structure with a single-stranded fragment, feature various characteristics with respect to size, toxicity, and permeability. Accordingly, aptamers are advantageous in terms of diagnosis and treatment and are materials that can be produced through relatively simple experiments. Systematic evolution of ligands by exponential enrichment (SELEX) is one of the most widely used experimental methods for generating aptamers; however, it is highly expensive and time-consuming. To reduce the related costs, recent studies have used in silico

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Gu, Bonwoo, and Yunsick Sung. "Enhanced Reinforcement Learning Method Combining One-Hot Encoding-Based Vectors for CNN-Based Alternative High-Level Decisions." Applied Sciences 11, no. 3 (2021): 1291. http://dx.doi.org/10.3390/app11031291.

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Gomoku is a two-player board game that originated in ancient China. There are various cases of developing Gomoku using artificial intelligence, such as a genetic algorithm and a tree search algorithm. Alpha-Gomoku, Gomoku AI built with Alpha-Go’s algorithm, defines all possible situations in the Gomoku board using Monte-Carlo tree search (MCTS), and minimizes the probability of learning other correct answers in the duplicated Gomoku board situation. However, in the tree search algorithm, the accuracy drops, because the classification criteria are manually set. In this paper, we propose an impr

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Delattre, Sylvain, and Nicolas Fournier. "On Monte-Carlo tree search for deterministic games with alternate moves and complete information." ESAIM: Probability and Statistics 23 (2019): 176–216. http://dx.doi.org/10.1051/ps/2018006.

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We consider a deterministic game with alternate moves and complete information, of which the issue is always the victory of one of the two opponents. We assume that this game is the realization of a random model enjoying some independence properties. We consider algorithms in the spirit of Monte-Carlo Tree Search, to estimate at best the minimax value of a given position: it consists in simulating, successively, n well-chosen matches, starting from this position. We build an algorithm, which is optimal, step by step, in some sense: once the n first matches are simulated, the algorithm decides

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Ayton, Benjamin, Brian Williams, and Richard Camilli. "Measurement Maximizing Adaptive Sampling with Risk Bounding Functions." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7511–19. http://dx.doi.org/10.1609/aaai.v33i01.33017511.

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In autonomous exploration a mobile agent must adapt to new measurements to seek high reward, but disturbances cause a probability of collision that must be traded off against expected reward. This paper considers an autonomous agent tasked with maximizing measurements from a Gaussian Process while subject to unbounded disturbances. We seek an adaptive policy in which the maximum allowed probability of failure is constrained as a function of the expected reward. The policy is found using an extension to Monte Carlo Tree Search (MCTS) which bounds probability of failure. We apply MCTS to a seque

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Spoerer, Kristian. "BI-DIRECTIONAL MONTE CARLO TREE SEARCH." Asia-Pacific Journal of Information Technology and Multimedia 10, no. 01 (2021): 17–26. http://dx.doi.org/10.17576/apjitm-2021-1001-02.

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This paper describes a new algorithm called Bi-Directional Monte Carlo Tree Search. The essential idea of Bi-directional Monte Carlo Tree Search is to run an MCTS forwards from the start state, and simultaneously run an MCTS backwards from the goal state, and stop when the two searches meet. Bi-Directional MCTS is tested on 8-Puzzle and Pancakes Problem, two single-agent search problems, which allow control over the optimal solution length d and average branching factor b respectively. Preliminary results indicate that enhancing Monte Carlo Tree Search by making it Bi-Directional speeds up the

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Lee, Jongmin, Wonseok Jeon, Geon-Hyeong Kim, and Kee-Eung Kim. "Monte-Carlo Tree Search in Continuous Action Spaces with Value Gradients." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4561–68. http://dx.doi.org/10.1609/aaai.v34i04.5885.

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Monte-Carlo Tree Search (MCTS) is the state-of-the-art online planning algorithm for large problems with discrete action spaces. However, many real-world problems involve continuous action spaces, where MCTS is not as effective as in discrete action spaces. This is mainly due to common practices such as coarse discretization of the entire action space and failure to exploit local smoothness. In this paper, we introduce Value-Gradient UCT (VG-UCT), which combines traditional MCTS with gradient-based optimization of action particles. VG-UCT simultaneously performs a global search via UCT with re

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Wang, Linnan, Yiyang Zhao, Yuu Jinnai, Yuandong Tian, and Rodrigo Fonseca. "Neural Architecture Search Using Deep Neural Networks and Monte Carlo Tree Search." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 06 (2020): 9983–91. http://dx.doi.org/10.1609/aaai.v34i06.6554.

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Neural Architecture Search (NAS) has shown great success in automating the design of neural networks, but the prohibitive amount of computations behind current NAS methods requires further investigations in improving the sample efficiency and the network evaluation cost to get better results in a shorter time. In this paper, we present a novel scalable Monte Carlo Tree Search (MCTS) based NAS agent, named AlphaX, to tackle these two aspects. AlphaX improves the search efficiency by adaptively balancing the exploration and exploitation at the state level, and by a Meta-Deep Neural Network (DNN)

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Vodopivec, Tom, Spyridon Samothrakis, and Branko Ster. "On Monte Carlo Tree Search and Reinforcement Learning." Journal of Artificial Intelligence Research 60 (December 20, 2017): 881–936. http://dx.doi.org/10.1613/jair.5507.

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Fuelled by successes in Computer Go, Monte Carlo tree search (MCTS) has achieved widespread adoption within the games community. Its links to traditional reinforcement learning (RL) methods have been outlined in the past; however, the use of RL techniques within tree search has not been thoroughly studied yet. In this paper we re-examine in depth this close relation between the two fields; our goal is to improve the cross-awareness between the two communities. We show that a straightforward adaptation of RL semantics within tree search can lead to a wealth of new algorithms, for which the trad

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Cheng, Yanqiu, Xianbiao Hu, Qing Tang, Hongsheng Qi, and Hong Yang. "Monte Carlo Tree Search-Based Mixed Traffic Flow Control Algorithm for Arterial Intersections." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 8 (2020): 167–78. http://dx.doi.org/10.1177/0361198120919746.

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A model-free approach is presented, based on the Monte Carlo tree search (MCTS) algorithm, for the control of mixed traffic flow of human-driven vehicles (HDV) and connected and autonomous vehicles (CAV), named MCTS-MTF, on a one-lane roadway with signalized intersection control. Previous research has often simplified the problem with certain assumptions to reduce computational burden, such as dividing a vehicle trajectory into several segments with constant speed or linear acceleration/deceleration, which was rather unrealistic. This study departs from the existing research in that minimum co

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Lu, Lina, Wanpeng Zhang, Xueqiang Gu, Xiang Ji, and Jing Chen. "HMCTS-OP: Hierarchical MCTS Based Online Planning in the Asymmetric Adversarial Environment." Symmetry 12, no. 5 (2020): 719. http://dx.doi.org/10.3390/sym12050719.

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The Monte Carlo Tree Search (MCTS) has demonstrated excellent performance in solving many planning problems. However, the state space and the branching factors are huge, and the planning horizon is long in many practical applications, especially in the adversarial environment. It is computationally expensive to cover a sufficient number of rewarded states that are far away from the root in the flat non-hierarchical MCTS. Therefore, the flat non-hierarchical MCTS is inefficient for dealing with planning problems with a long planning horizon, huge state space, and branching factors. In this work

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Baier, Hendrik, and Mark H. M. Winands. "MCTS-Minimax Hybrids with State Evaluations." Journal of Artificial Intelligence Research 62 (June 7, 2018): 193–231. http://dx.doi.org/10.1613/jair.1.11208.

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   Monte-Carlo Tree Search (MCTS) has been found to show weaker play than minimax-based search in some tactical game domains. This is partly due to its highly selective search and averaging value backups, which make it susceptible to traps. In order to combine the strategic strength of MCTS and the tactical strength of minimax, MCTS-minimax hybrids have been introduced, embedding shallow minimax searches into the MCTS framework. Their results have been promising even without making use of domain knowledge such as heuristic evaluation functions. This article continues this lin

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Wang, Xiaoxue, Yujie Qian, Hanyu Gao, et al. "Towards efficient discovery of green synthetic pathways with Monte Carlo tree search and reinforcement learning." Chemical Science 11, no. 40 (2020): 10959–72. http://dx.doi.org/10.1039/d0sc04184j.

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Kim, Beomjoon, Kyungjae Lee, Sungbin Lim, Leslie Kaelbling, and Tomas Lozano-Perez. "Monte Carlo Tree Search in Continuous Spaces Using Voronoi Optimistic Optimization with Regret Bounds." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 06 (2020): 9916–24. http://dx.doi.org/10.1609/aaai.v34i06.6546.

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Many important applications, including robotics, data-center management, and process control, require planning action sequences in domains with continuous state and action spaces and discontinuous objective functions. Monte Carlo tree search (MCTS) is an effective strategy for planning in discrete action spaces. We provide a novel MCTS algorithm (voot) for deterministic environments with continuous action spaces, which, in turn, is based on a novel black-box function-optimization algorithm (voo) to efficiently sample actions. The voo algorithm uses Voronoi partitioning to guide sampling, and i

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Fabbri, André, Frédéric Armetta, Éric Duchêne, and Salima Hassas. "A Self-Acquiring Knowledge Process for MCTS." International Journal on Artificial Intelligence Tools 25, no. 01 (2016): 1660007. http://dx.doi.org/10.1142/s0218213016600071.

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MCTS (Monte Carlo Tree Search) is a well-known and efficient process to cover and evaluate a large range of states for combinatorial problems. We choose to study MCTS for the Computer Go problem, which is one of the most challenging problem in the field of Artificial Intelligence. For this game, a single combinatorial approach does not always lead to a reliable evaluation of the game states. In order to enhance MCTS ability to tackle such problems, one can benefit from game specific knowledge in order to increase the accuracy of the game state evaluation. Such a knowledge is not easy to acquir

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Agárdi, Anita, and Károly Nehéz. "PARALLEL MACHINE SCHEDULING WITH MONTE CARLO TREE SEARCH." Acta Polytechnica 61, no. 2 (2021): 307–12. http://dx.doi.org/10.14311/ap.2021.61.0307.

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In this article, a specific production scheduling problem (PSP), the Parallel Machine Scheduling Problem (PMSP) with Job and Machine Sequence Setup Times, Due Dates and Maintenance Times is presented. In this article after the introduction and literature review the mathematical model of the Parallel Machines Scheduling Problem with Job and Machine Sequence Setup Times, Due Dates and Maintenance Times is presented. After that the Monte Carlo Tree Search and Simulated Annealing are detailed. Our representation technique and its evaluation are also introduced. After that, the efficiency of the al

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Brown, Christopher, Vladimir Janjic, M. Goli, and J. McCall. "Programming Heterogeneous Parallel Machines Using Refactoring and Monte–Carlo Tree Search." International Journal of Parallel Programming 48, no. 4 (2020): 583–602. http://dx.doi.org/10.1007/s10766-020-00665-z.

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Abstract This paper presents a new technique for introducing and tuning parallelism for heterogeneous shared-memory systems (comprising a mixture of CPUs and GPUs), using a combination of algorithmic skeletons (such as farms and pipelines), Monte–Carlo tree search for deriving mappings of tasks to available hardware resources, and refactoring tool support for applying the patterns and mappings in an easy and effective way. Using our approach, we demonstrate easily obtainable, significant and scalable speedups on a number of case studies showing speedups of up to 41 over the sequential code on

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Ontañón, Santiago. "Combinatorial Multi-armed Bandits for Real-Time Strategy Games." Journal of Artificial Intelligence Research 58 (March 29, 2017): 665–702. http://dx.doi.org/10.1613/jair.5398.

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Games with large branching factors pose a significant challenge for game tree search algorithms. In this paper, we address this problem with a sampling strategy for Monte Carlo Tree Search (MCTS) algorithms called "naive sampling", based on a variant of the Multi-armed Bandit problem called "Combinatorial Multi-armed Bandits" (CMAB). We analyze the theoretical properties of several variants of naive sampling, and empirically compare it against the other existing strategies in the literature for CMABs. We then evaluate these strategies in the context of real-time strategy (RTS) games, a genre o

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Hostetler, Jesse, Alan Fern, and Thomas Dietterich. "Sample-Based Tree Search with Fixed and Adaptive State Abstractions." Journal of Artificial Intelligence Research 60 (December 14, 2017): 717–77. http://dx.doi.org/10.1613/jair.5483.

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Sample-based tree search (SBTS) is an approach to solving Markov decision problems based on constructing a lookahead search tree using random samples from a generative model of the MDP. It encompasses Monte Carlo tree search (MCTS) algorithms like UCT as well as algorithms such as sparse sampling. SBTS is well-suited to solving MDPs with large state spaces due to the relative insensitivity of SBTS algorithms to the size of the state space. The limiting factor in the performance of SBTS tends to be the exponential dependence of sample complexity on the depth of the search tree. The number of sa

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Haraszti, Sándor, Bálint Kővári, Máté Kolat, et al. "Area Scanning with Reinforcement Learning and MCTS in Smart City Applications." Repüléstudományi Közlemények 32, no. 2 (2020): 137–53. http://dx.doi.org/10.32560/rk.2020.2.10.

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This research project is focused on area scanning in the scope of smart city applications with unmanned aerial vehicles or UAVs. More powerful devices have been designed in terms of range, capacity and sensory capabilities in the recent years. This makes possible easier automation, thus suppressing the need for human resources. Some of the fields of applications include: traffic or pollution monitoring, land surveying, civil security control or natural disaster control and monitoring. With the increased number of UAV applications, the use and development of efficient algorithms is more and mor

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Wu, Keshou, Guanfeng Liu, and Junwen Lu. "Graph-Based Node Finding in Big Complex Contextual Social Graphs." Complexity 2020 (February 26, 2020): 1–13. http://dx.doi.org/10.1155/2020/7909826.

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Graph pattern matching is to find the subgraphs matching the given pattern graphs. In complex contextual social networks, considering the constraints of social contexts like the social relationships, the social trust, and the social positions, users are interested in the top-K matches of a specific node (denoted as the designated node) based on a pattern graph, rather than the entire set of graph matching. This inspires the conText-Aware Graph pattern-based top-K designated node matching (TAG-K) problem, which is NP-complete. Targeting this challenging problem, we propose a recurrent neural ne

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Thanh, Vo Hong, and Roberto Zunino. "Adaptive tree-based search for stochastic simulation algorithm." International Journal of Computational Biology and Drug Design 7, no. 4 (2014): 341. http://dx.doi.org/10.1504/ijcbdd.2014.066542.

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Kucharski, Bryon, Azad Deihim, and Mehmet Ergezer. "Machine Learning Based Heuristic Search Algorithms to Solve Birds of a Feather Card Game." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9656–61. http://dx.doi.org/10.1609/aaai.v33i01.33019656.

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This research was conducted by an interdisciplinary team of two undergraduate students and a faculty to explore solutions to the Birds of a Feather (BoF) Research Challenge. BoF is a newly-designed perfect-information solitaire-type game. The focus of the study was to design and implement different algorithms and evaluate their effectiveness. The team compared the provided depth-first search (DFS) to heuristic algorithms such as Monte Carlo tree search (MCTS), as well as a novel heuristic search algorithm guided by machine learning. Since all of the studied algorithms converge to a solution fr

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Cheng, Yuxia, Zhiwei Wu, Kui Liu, Qing Wu, and Yu Wang. "Smart DAG Tasks Scheduling between Trusted and Untrusted Entities Using the MCTS Method." Sustainability 11, no. 7 (2019): 1826. http://dx.doi.org/10.3390/su11071826.

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Task scheduling is critical for improving system performance in the distributed heterogeneous computing environment. The Directed Acyclic Graph (DAG) tasks scheduling problem is NP-complete and it is hard to find an optimal schedule. Due to its key importance, the DAG tasks scheduling problem has been extensively studied in the literature. However, many previously proposed traditional heuristic algorithms are usually based on greedy methods and also lack the consideration of scheduling tasks between trusted and untrusted entities, which makes the problem more complicated, but there still exist

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Kumagai, Kaori, Ichiro Kobayashi, Daichi Mochihashi, Hideki Asoh, Tomoaki Nakamura, and Takayuki Nagai. "Natural Language Generation Using Monte Carlo Tree Search." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 5 (2018): 777–85. http://dx.doi.org/10.20965/jaciii.2018.p0777.

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We propose a method of simulation-based natural language generation that accounts for both building a correct syntactic structure and reflecting the given situational information as input for the generated sentence. We employ the Monte Carlo tree search for this nontrivial search problem in simulation, using context-free grammar rules as search operators. We evaluated numerous generation results from two aspects: the appropriateness of sentence contents for the given input information and the sequence of words in a generated sentence. Furthermore, in order to realize an efficient search in sim

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Tarrataca, Luís, and Andreas Wichert. "Tree search and quantum computation." Quantum Information Processing 10, no. 4 (2010): 475–500. http://dx.doi.org/10.1007/s11128-010-0212-z.

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Lim, Hyesook, Ha Chu, and Changhoon Yim. "Hierarchical Binary Search Tree for Packet Classification." IEEE Communications Letters 11, no. 8 (2007): 689–91. http://dx.doi.org/10.1109/lcomm.2007.070389.

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Shelar, Vaibhav, Selamani Subramani, and Jebaseelan Davidson. "R-tree data structure implementation for Computer Aided Engineering (CAE) tools." International Journal for Simulation and Multidisciplinary Design Optimization 12 (2021): 6. http://dx.doi.org/10.1051/smdo/2021005.

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Searching and handling geometric data are basic requirements of any Computer Aided Engineering application (CAE). Spatial search and local search has greater importance in CAD and CAE applications for reducing the model preparation time. There are many efficient algorithms being made to search geometrical data. Current neighbour search strategy is limited and not efficient in different CAE platforms. R-tree is tree data structure used for spatial access methods. This paper presents a review of R-tree data structure with its implementation in one of the CAE tool for neighbour search and local s

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Guo, Zhenyang, Xuan Wang, Shuhan Qi, Tao Qian, and Jiajia Zhang. "Heuristic Sensing: An Uncertainty Exploration Method in Imperfect Information Games." Complexity 2020 (October 24, 2020): 1–9. http://dx.doi.org/10.1155/2020/8815770.

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Imperfect information games have served as benchmarks and milestones in fields of artificial intelligence (AI) and game theory for decades. Sensing and exploiting information to effectively describe the game environment is of critical importance for game solving, besides computing or approximating an optimal strategy. Reconnaissance blind chess (RBC), a new variant of chess, is a quintessential game of imperfect information where the player’s actions are definitely unobserved by the opponent. This characteristic of RBC exponentially expands the scale of the information set and extremely invoke

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Carvalho, Alda, Nuno Crato, and Carla Gomes. "A generative power-law search tree model." Computers & Operations Research 36, no. 8 (2009): 2376–86. http://dx.doi.org/10.1016/j.cor.2008.08.017.

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Xu, Kui, Zhe Wang, Jianping Shi, Hongsheng Li, and Qiangfeng Cliff Zhang. "A2-Net: Molecular Structure Estimation from Cryo-EM Density Volumes." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1230–37. http://dx.doi.org/10.1609/aaai.v33i01.33011230.

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Constructing of molecular structural models from CryoElectron Microscopy (Cryo-EM) density volumes is the critical last step of structure determination by Cryo-EM technologies. Methods have evolved from manual construction by structural biologists to perform 6D translation-rotation searching, which is extremely compute-intensive. In this paper, we propose a learning-based method and formulate this problem as a vision-inspired 3D detection and pose estimation task. We develop a deep learning framework for amino acid determination in a 3D Cryo-EM density volume. We also design a sequence-guided

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Zhang, Jia Jia, Xuan Wang, Lin Yao, Jing Peng Li, and Xue Dong Shen. "Modified UCT Algorithm with Risk Dominance Methods in Imperfect Information Game." Applied Mechanics and Materials 610 (August 2014): 367–76. http://dx.doi.org/10.4028/www.scientific.net/amm.610.367.

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UCT (Upper confidential bounds on Trees) has been applied quite well as a selection approach in MCTS(Monte Carlo Tree Search) in imperfect information games like poker. By using risk dominance as complementary part of decision method besides payoff dominance, opponent strategies is better characterized as their risk factors, like bluff actions in Texas Hold’em Poker . In this paper, estimation method about the influence of risk factors on computing game equilibrium is provided. A novel algorithm, UCT-risk is proposed as modification about UCT algorithm basing on risk estimation methods. To ver

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Cheng, Yanqiu, Chenxi Chen, Xianbiao Hu, Kuanmin Chen, Qing Tang, and Yang Song. "Enhancing Mixed Traffic Flow Safety via Connected and Autonomous Vehicle Trajectory Planning with a Reinforcement Learning Approach." Journal of Advanced Transportation 2021 (June 12, 2021): 1–11. http://dx.doi.org/10.1155/2021/6117890.

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The longitudinal trajectory planning of connected and autonomous vehicle (CAV) has been widely studied in the literature to reduce travel time or fuel consumptions. The safety impact of CAV trajectory planning to the mixed traffic flow with both CAV and human-driven vehicle (HDV), however, is not well understood yet. This study presents a reinforcement learning modeling approach, named Monte Carlo tree search-based autonomous vehicle safety algorithm, or MCTS-AVS, to optimize the safety of mixed traffic flow, on a one-lane roadway with signalized intersection control. Crash potential index (CP

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W. DeBry, Richard G. Olmstead, Ronald. "A Simulation Study of Reduced Tree-Search Effort in Bootstrap Resampling Analysis." Systematic Biology 49, no. 1 (2000): 171–79. http://dx.doi.org/10.1080/10635150050207465.

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Hu, Zhong Yue. "Research on Anti-Collision Algorithm of Short Distance Data Communication Based on Binary-Tree Disassembly." Applied Mechanics and Materials 686 (October 2014): 354–58. http://dx.doi.org/10.4028/www.scientific.net/amm.686.354.

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This paper puts forward adaptive anti collision algorithm based on two fork tree decomposition. New search algorithm built on the basis of binary-tree algorithm, using the uniqueness of the label EPC, to estimate the distribution of label by slot allocation, the huge and complicated two fork tree is decomposed into several simple binary-tree by search the collision slots for binary-tree, so, it can simplifies the search process. The algorithm fully considers4 important performance parameters of the reader paging times, transmission delay, energy consumption and throughput label, the simulation

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Magid, Evgeni, Takashi Tsubouchi, Eiji Koyanagi, and Tomoaki Yoshida. "Building a Search Tree for a Pilot System of a Rescue Search Robot in a Discretized Random Step Environment." Journal of Robotics and Mechatronics 23, no. 4 (2011): 567–81. http://dx.doi.org/10.20965/jrm.2011.p0567.

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Rescue robotics applies search and rescue robots to expand rescue capabilities while increasing safety. Mobile robots working at a disaster site are monitored remotely by operators who may not be able to see the site well and select work paths appropriately. Our goal is to provide a “pilot system” that can propose options for traversing 3D debris environments. This requires a special debris path search algorithm and an appropriately defined search tree ensuring smooth exploration. To make a path search feasible in huge real state space we discretize search space and robot movement before a sea

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Néron, Emmanuel, Fabrice Tercinet, and Francis Sourd. "Search tree based approaches for parallel machine scheduling." Computers & Operations Research 35, no. 4 (2008): 1127–37. http://dx.doi.org/10.1016/j.cor.2006.07.008.

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Shin, Kento, Duy Phuoc Tran, Kazuhiro Takemura, Akio Kitao, Kei Terayama, and Koji Tsuda. "Enhancing Biomolecular Sampling with Reinforcement Learning: A Tree Search Molecular Dynamics Simulation Method." ACS Omega 4, no. 9 (2019): 13853–62. http://dx.doi.org/10.1021/acsomega.9b01480.

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Wang, Danping, Kunyuan Hu, Lianbo Ma, Maowei He, and Hanning Chen. "Multispecies Coevolution Particle Swarm Optimization Based on Previous Search History." Discrete Dynamics in Nature and Society 2017 (2017): 1–22. http://dx.doi.org/10.1155/2017/5193013.

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A hybrid coevolution particle swarm optimization algorithm with dynamic multispecies strategy based on K-means clustering and nonrevisit strategy based on Binary Space Partitioning fitness tree (called MCPSO-PSH) is proposed. Previous search history memorized into the Binary Space Partitioning fitness tree can effectively restrain the individuals’ revisit phenomenon. The whole population is partitioned into several subspecies and cooperative coevolution is realized by an information communication mechanism between subspecies, which can enhance the global search ability of particles and avoid p

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Yang, Zhen Yu, Juan Xing, and Xin Gang Wang. "Segment Slot Partial Competitive Anti-Collision Algorithm for RFID System." Applied Mechanics and Materials 148-149 (December 2011): 753–56. http://dx.doi.org/10.4028/www.scientific.net/amm.148-149.753.

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Slot partial competitive (SPC) anti-collision algorithm is based on the ISO/IEC18000-6C UHF radio frequency identification (RFID) standard, which introduces dynamic binary tree search technology into the competitive collision avoidance mechanism. Moreover, the SPC algorithm applies the specific technologies to idle slots and collision slots. This paper proposes a segment slot partial competitive (SSPC) anti-collision algorithm based on the SPC, and introduce the segment algorithm. The performances of the dynamic binary tree search, the SPC algorithm and the SSPC algorithm are compared via simu

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Forster, Florian, and Andreas Bortfeldt. "A tree search procedure for the container relocation problem." Computers & Operations Research 39, no. 2 (2012): 299–309. http://dx.doi.org/10.1016/j.cor.2011.04.004.

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Cruz Chvez, Marco Antonio, and Alina Martnez Oropeza. "B-Tree Algorithm Complexity Analysis to Evaluate the Feasibility of its Application in the University Course Timetabling Problem." Journal of Artificial Intelligence and Soft Computing Research 3, no. 4 (2013): 251–63. http://dx.doi.org/10.2478/jaiscr-2014-0018.

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Abstract This paper presents a comparative analysis of complexity between the B-TREE and the Binary Search Algorithms, both theoretically and experimentally, to evaluate their efficiency in finding overlap of classes for students and teachers in the University Course Timetabling Problem (UCTP). According to the theory, B-TREE Search complexity is lower than Binary Search. The performed experimental tests showed the B-TREE Search Algorithm is more efficient than Binary Search, but only using a dataset larger than 75 students per classroom.

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Bai, Le Qiang, and Xi Yang. "An Parallel Anti-Collision Algorithm Based on Adaptive Multi-Tree Search." Applied Mechanics and Materials 556-562 (May 2014): 3707–10. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3707.

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In order to improve the unstable system throughput and reduce the "misjudgment" issues, this paper proposes a kind of parallel multi-tree search anti-collision algorithm. The algorithm embeds pseudo collision time slot with OVSF spreading code in RFID systems for identifying tags. As there are more than one code channels in a pseudo collision time slot, the slot can identify tags concurrently, reducing the accumulated collision. Theoretic analysis proves that the system throughput is improved with the increasing of code length. Simulation results show that the new algorithm has more stable and

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Zheng, Lei, Jianbo Hu, and Shukui Xu. "Marine Search and Rescue of UAV in Long-Distance Security Modeling Simulation." Polish Maritime Research 24, s3 (2017): 192–99. http://dx.doi.org/10.1515/pomr-2017-0122.

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Abstract Long-distance safety of Marine search and rescue using drones can improve the searching speed. The current method is based on the long distance security classification of UAV.The degree of accuracy is low. A long-distance security modeling approach based on ArduinoMiniPro’s Marine search-and-rescue applying UAV is proposed. The method puts the fault tree analysis and relevant calculation for risk identification into use. The main factors affecting the safety of unmanned aerial vehicle (UAV) are long-distance searching and rescuing. The experimental results show that the proposed metho

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Consoli, S., K. Darby-Dowman, N. Mladenović, and J. A. Moreno Pérez. "Greedy Randomized Adaptive Search and Variable Neighbourhood Search for the minimum labelling spanning tree problem." European Journal of Operational Research 196, no. 2 (2009): 440–49. http://dx.doi.org/10.1016/j.ejor.2008.03.014.

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Winblad, Kjell, Konstantinos Sagonas, and Bengt Jonsson. "Lock-free Contention Adapting Search Trees." ACM Transactions on Parallel Computing 8, no. 2 (2021): 1–38. http://dx.doi.org/10.1145/3460874.

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Concurrent key-value stores with range query support are crucial for the scalability and performance of many applications. Existing lock-free data structures of this kind use a fixed synchronization granularity. Using a fixed synchronization granularity in a concurrent key-value store with range query support is problematic as the best performing synchronization granularity depends on a number of factors that are difficult to predict, such as the level of contention and the number of items that are accessed by range queries. We present the first linearizable lock-free key-value store with rang

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