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Dissertations / Theses on the topic 'Monte Carlo tree search'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2025

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1

Limér, Christoffer, and Erik Kalmér. "Monte Carlo Tree Search for Risk." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-297695.

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The idea of using artificial intelligence to evaluatemilitary strategies is relevant for a large number of governmentstoday. With programs like AlphaZero beating world championsin games of ever-increasing complexity, military adaptations areprobably not far away, if they are not in use already. Partof these programs’ recent success is due to a heuristic searchalgorithm called Monte Carlo Tree Search. In this project,we explored the possibility of using this algorithm to build aprogram capable of playing the strategy board game of Riskat a high level. The complexity and stochastic dynamic ofthe
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2

Sista, Subrahmanya Srivathsava. "Adversarial Game Playing Using Monte Carlo Tree Search." University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1479820656701076.

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3

Roucairol, Milo. "Monte-Carlo tree search applied to structure generation." Electronic Thesis or Diss., Université Paris sciences et lettres, 2024. http://www.theses.fr/2024UPSLD029.

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Ce document regroupe les article publiés lors de ma thèse dirigée par Tristan Cazenave au LAMSADE. La recherche Monte Carlo désigne une classe d'algorithmes de recherche stochastiques retournant une solution avec une garantie dans le temps, mais sans garantie de résultat. Ces algorithmes utilisent des techniques d'apprentissage par renforcement basées sur des exploration aléatoires ou guidées. Les capacités des algorithmes Monte Carlo sont limitées dans des domaines d'application mis en valeur récemment, comme la génération d'image et de texte, ou les réseaux de neurones, LLM et autres algorit
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4

Zhou, Jun. "Parallel Go on CUDA with Monte Carlo Tree Search." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1367942396.

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5

Cheng, Chee Chian. "RESOURCE CONSTRAINT COOPERATIVE GAME WITH MONTE CARLO TREE SEARCH." OpenSIUC, 2016. https://opensiuc.lib.siu.edu/dissertations/1239.

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A hybrid methodology of game theory and Monte Carlo Tree Search was developed and the hybrid methodology was tested with various case studies through the nurse scheduling problem to show that it was able to form Pareto front dominance solutions, finding feasible solutions that were optimal and finding feasible partial solutions in over-constrained problems. The performance comparison was carried out with the Genetic Algorithm on the Resident Physician Scheduling problem and showed that the hybrid methodology was able to produce better quality solutions compared to the state of the art approach.
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Bergh, Peter. "Domain independent enhancements to Monte Carlo tree search for eurogames." Thesis, Mittuniversitetet, Institutionen för data- och systemvetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-41251.

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The Monte Carlo tree search-algorithm (MCTS) has been proven successful when applied to combinatorial games, a term applied to sequential games with perfect information. As the focus for MCTS has tended to lean towards combinatorial games, general MCTS-strategies for other types of board games are hard to find. On another front, board games under the name of “Eurogames” have become increasingly popular in the last decade. These games introduce yet another set of challenges for game-playing agents on top of what combinatorial games already offer. Since its initial conception, a large number of
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7

Rimmel, Arpad. "Improvements and Evaluation of the Monte Carlo Tree Search Algorithm." Paris 11, 2009. http://www.theses.fr/2009PA112223.

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Ma thèse se situe dans le contexte de la planification à horizon fini en environnement discret avec un nombre d'états trop important pour qu'ils soient tous explorés. L'objectif est de maximiser une fonction de récompense qui associe une valeur aux états finaux. Cette thèse est en particulier centrée sur l'amélioration et l'étude d'un nouvel algorithme: l'exploration d'arbre basée sur une formule de bandit avec évaluation Monte Carlo. Après avoir présenté les algorithmes de référence du domaine (Minimax et Alphabéta dans le cas deux joueurs; Nested Monte Carlo et Programmation Dynamique dans l
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8

Whitehouse, Daniel. "Monte Carlo Tree Search for games with hidden information and uncertainty." Thesis, University of York, 2014. http://etheses.whiterose.ac.uk/8117/.

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Monte Carlo Tree Search (MCTS) is an AI technique that has been successfully applied to many deterministic games of perfect information, leading to large advances in a number of domains, such as Go and General Game Playing. Imperfect information games are less well studied in the field of AI despite being popular and of significant commercial interest, for example in the case of computer and mobile adaptations of turn based board and card games. This is largely because hidden information and uncertainty leads to a large increase in complexity compared to perfect information games. In this thes
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9

Pacaud, Alexandre. "Bidding efficiently in Simultaneous Ascending Auctions using Monte Carlo Tree Search." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAT003.

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Depuis son introduction en 1994 aux Etats-Unis, l’enchère ascendante simultanée (SAA) est devenue le mécanisme privilégié pour les enchères du spectre licencié. Avec des investissements dépassant parfois le milliard d’euros, une stratégie d’enchérissement performante devient cruciale pour les opérateurs mobiles. Malgré son importance, il existe un manque de recherche dédiée à la création d’une stratégie d’enchérissement performante dans le cadre du SAA. La complexité intrinsèque du jeu associé à l’enchère SAA rend son analyse ardue pour la théorie des enchères et les méthodes exactes de résolu
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10

Goh, Say Leng. "An investigation of Monte Carlo tree search and local search for course timetabling problems." Thesis, University of Nottingham, 2017. http://eprints.nottingham.ac.uk/43558/.

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The work presented in this thesis focuses on solving course timetabling problems, a variant of education timetabling. Automated timetabling is a popular topic among researchers and practitioners because manual timetable construction is impractical, if not impossible, as it is known to be NP-hard. A two-stage approach is investigated. The first stage involves finding feasible solutions. Monte Carlo Tree Search (MCTS) is utilized in this stage. As far as we are aware, it is used for the first time in addressing the timetabling problem. It is a relatively new search method and has achieved breakt
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Bolin, Jakob, and Nico Palmroos. "Monte-Carlo Tree Search Used for Fortification in the Game of Risk." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-297698.

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The strategy game Risk is a very popular boardgame, requiring little effort to learn but lots of skill to master.The aim of this project is to explore the fortification phase of thegame, where the player’s troops are moved between territories.Our method is based on adapting Monte Carlo tree search(MCTS) to Risk. To improve the troop movements, we proposetwo techniques, hierarchical search and progressive bias. Thesemethods, combined with other extensions of MCTS are thencompared against a baseline player of the game. Our results showthat hierarchical search improved the MCTS agent’s playingpow
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12

Couetoux, Adrien. "Monte Carlo Tree Search for Continuous and Stochastic Sequential Decision Making Problems." Thesis, Paris 11, 2013. http://www.theses.fr/2013PA112192.

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Dans cette thèse, nous avons étudié les problèmes de décisions séquentielles, avec comme application la gestion de stocks d'énergie. Traditionnellement, ces problèmes sont résolus par programmation dynamique stochastique. Mais la grande dimension, et la non convexité du problème, amènent à faire des simplifications sur le modèle pour pouvoir faire fonctionner ces méthodes.Nous avons donc étudié une méthode alternative, qui ne requiert pas de simplifications du modèle: Monte Carlo Tree Search (MCTS). Nous avons commencé par étendre le MCTS classique (qui s’applique aux domaines finis et détermi
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13

Frendin, Carl, and Andreas Sjöroos. "Go Go! - Evaluating Different Variants of Monte Carlo Tree Search for Playing Go." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-157520.

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Monte Carlo Tree Search (MCTS) is a Go algorithm that is used in many recent strong go playing agents. In this report we test and compare different algorithms related to Monte Carlo simulation, seeing how well they do against each other under different time constraints on consumer hardware. This is done with our own implementation of Go rules and algorithms written in Java. The All Moves As First (AMAF) algorithmhad the best performance in the performed tests.
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Klein, Simon. "Attacking SameGame using Monte-Carlo Tree Search : Using randomness as guidance in puzzles." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-167145.

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Since the birth of the computer, the creation of algorithms to beat humans in games has been a hot topic, algorithms that if successful often have vast implications. For some games and problems however, it has been notoriously difficult for computers to perform well. But in 2006 an algorithm known as Monte-Carlo Tree Search (MCTS) was invented that boosted the performance of computers in some of the difficult two-player games, e.g. computer Go. Around the same time researchers started to look into how MCTS could be applied to single-player domains, one of them was the puzzle of SameGame. This
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15

Jönsson, Jonatan, and Felix Stenbäck. "Monte-Carlo Tree Search in Continuous Action Spaces for Autonomous Racing : F1-tenth." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-42442.

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Autonomous cars involve problems with control and planning. In thispaper, we implement and evaluate an autonomous agent based ona Monte-Carlo Tree Search in continuous action space. To facilitatethe algorithm, we extend an existing simulation framework and usea GPU for faster calculations. We compare three action generatorsand two rewards functions. The results show that MCTS convergesto an effective driving agent in static environments. However, it onlysucceeds at driving slow speeds in real-time. We discuss the problemsthat arise in dynamic and static environments and look to future workin i
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Holmgren, Viktor. "General-purpose maintenance planning using deep reinforcement learning and Monte Carlo tree search." Thesis, Linköpings universitet, Artificiell intelligens och integrerade datorsystem, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-163866.

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Maintenance planning and execution is increasingly important for the modern industrial sector. Maintenance costs can amount to a major part of industrial spending. However, it is not as simple as just reducing maintenance budgets. A balance must be struck between risking unplanned downtime and the costs of maintenance efforts, in order to keep the profit margins needed to compete in the global markets of today. One approach to improve the effectiveness of industries is to apply intelligent maintenance planners. In this thesis, a general-purpose maintenance planner based on Monte-Carlotree sear
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17

Bosc, Guillaume. "Anytime discovery of a diverse set of patterns with Monte Carlo tree search." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSEI074/document.

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La découverte de motifs qui caractérisent fortement une classe vis à vis d'une autre reste encore un problème difficile en fouille de données. La découverte de sous-groupes (Subgroup Discovery, SD) est une approche formelle de fouille de motifs qui permet la construction de classifieurs intelligibles mais surtout d'émettre des hypothèses sur les données. Cependant, cette approche fait encore face à deux problèmes majeurs : (i) comment définir des mesures de qualité appropriées pour caractériser l'intérêt d'un motif et (ii) comment sélectionner une méthode heuristique adaptée lorsqu’une énuméra
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18

Couetoux, Adrien. "Monte Carlo Tree Search pour les problèmes de décision séquentielle en milieu continus et stochastiques." Phd thesis, Université Paris Sud - Paris XI, 2013. http://tel.archives-ouvertes.fr/tel-00927252.

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Dans cette thèse, nous avons étudié les problèmes de décisions séquentielles, avec comme application la gestion de stocks d'énergie. Traditionnellement, ces problèmes sont résolus par programmation dynamique stochastique. Mais la grande dimension, et la non convexité du problème, amènent à faire des simplifications sur le modèle pour pouvoir faire fonctionner ces méthodes. Nous avons donc étudié une méthode alternative, qui ne requiert pas de simplifications du modèle: Monte Carlo Tree Search (MCTS). Nous avons commencé par étendre le MCTS classique (qui s'applique aux domaines finis et déterm
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19

Eriksson, Patrik. "MONTE CARLO TREE SEARCH OCH MINIMAX : En jämförelse i tidseffektivitet i ett matcha-3-spel." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-15412.

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I arbetet implementerades två algoritmer som utvärderades genom att spela ett matcha-3-spel mot varandra. Den första algoritmen var Minimax som väljer sina drag genom att evaluera de möjliga dragen från ett tillstånd. För att inte sökrymden ska bli för stor begränsas algoritmen med ett sökdjup. Den andra algoritmen var en MCTS som utför flera simuleringar där den utför slumpmässiga drag till ett spelslut för att få en uppskattning över resultatet från de olika dragen. Spelet som de utvärderas på är av typen matcha-3-battle. Flera experiment utfördes sedan på de två algoritmerna, där de spelade
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20

Poromaa, Erik Ragnar. "Crushing Candy Crush : Predicting Human Success Rate in a Mobile Game using Monte-Carlo Tree Search." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-206595.

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The purpose of this thesis is to evaluate the possibility of predicting difficulty, measured in average human success rate (AHSR), across game levels of a mobile game using a general AI algorithm. We implemented and tested a simulation based bot using MCTS for Candy. Our results indicate that AHSR can be predicted accurately using MCTS, which in turn suggests that our bot could be used to streamline game level development. Our work is relevant to the field of AI, especially the subfields of MCTS and single-player stochastic games as Candy, with its diverse set of features, proved an excellent
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21

Fabbri, André. "Dynamique d'apprentissage pour Monte Carlo Tree Search : applications aux jeux de Go et du Clobber solitaire impartial." Thesis, Lyon 1, 2015. http://www.theses.fr/2015LYO10183/document.

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Depuis son introduction pour le jeu de Go, Monte Carlo Tree Search (MCTS) a été appliqué avec succès à d'autres jeux et a ouvert la voie à une famille de nouvelles méthodes comme Mutilple-MCTS ou Nested Monte Carlo. MCTS évalue un ensemble de situations de jeu à partir de milliers de fins de parties générées aléatoirement. À mesure que les simulations sont produites, le programme oriente dynamiquement sa recherche vers les coups les plus prometteurs. En particulier, MCTS a suscité l'intérêt de la communauté car elle obtient de remarquables performances sans avoir pour autant recours à de nombr
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22

Granberg, Orrevall Aron. "Monto Carlo Tree Search in Real Time Strategy Games with Applications to Starcraft 2." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-263150.

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This thesis presents an architecture for an agent that can play the real-time strategy game Starcraft 2 (SC2) by applying Monte Carlo Tree Search (MCTS) together with genetic algorithms and machine learning methods. Together with the MCTS search, a light-weight and accurate combat simulator for SC2 as well as a build order optimizer are presented as independent modules. While MCTS has been well studied for turn-based games such as Go and Chess, its performance has so far been less explored in the context of real-time games. Using machine learning and planning methods in real-time strategy game
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23

Nunes, Cecília. "Towards the improvement of decision tree learning: a perspective on search and evaluation." Doctoral thesis, Universitat Pompeu Fabra, 2019. http://hdl.handle.net/10803/667879.

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Data mining and machine learning (ML) are increasingly at the core of many aspects of modern life. With growing concerns about the impact of relying on predictions we cannot understand, there is widespread agreement regarding the need for reliable interpretable models. One of the areas where this is particularly important is clinical decision-making. Specifically, explainable models have the potential to facilitate the elaboration of clinical guidelines and related decision-support tools. The presented research focuses on the improvement of decision tree (DT) learning, one of the most popular
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Saffidine, Abdallah. "Solving Games and All That." Phd thesis, Université Paris Dauphine - Paris IX, 2013. http://tel.archives-ouvertes.fr/tel-01022750.

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Efficient best-first search algorithms have been developed for deterministic two-player games with two-outcome.We present a formal framework to represent such best-first search algorithms.The framework is general enough to express popular algorithms such as Proof Number Search, Monte Carlo Tree Search, and the Product Propagation algorithm.We then show how a similar framework can be devised for two more general settings: two-player games with multiple outcomes, and the model checking problem in modal logic K.This gives rise to new Proof Number and Monte Carlo inspired search algorithms for the
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Teytaud, Fabien. "Introduction of statistics in optimization." Phd thesis, Université Paris Sud - Paris XI, 2011. http://tel.archives-ouvertes.fr/tel-00655731.

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In this thesis we study two optimization fields. In a first part, we study the use of evolutionary algorithms for solving derivative-free optimization problems in continuous space. In a second part we are interested in multistage optimization. In that case, we have to make decisions in a discrete environment with finite horizon and a large number of states. In this part we use in particular Monte-Carlo Tree Search algorithms. In the first part, we work on evolutionary algorithms in a parallel context, when a large number of processors are available. We start by presenting some state of the art
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Wang, Yuan. "Mastering the Game of Gomoku without Human Knowledge." DigitalCommons@CalPoly, 2018. https://digitalcommons.calpoly.edu/theses/1865.

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Gomoku, also called Five in a row, is one of the earliest checkerboard games invented by humans. For a long time, it has brought countless pleasures to us. We humans, as players, also created a lot of skills in playing it. Scientists normalize and enter these skills into the computer so that the computer knows how to play Gomoku. However, the computer just plays following the pre-entered skills, it doesn’t know how to develop these skills by itself. Inspired by Google’s AlphaGo Zero, in this thesis, by combining the technologies of Monte Carlo Tree Search, Deep Neural Networks, and Reinforceme
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McKinley, Nathan D. "A Decision Theoretic Approach to Natural Language Generation." Case Western Reserve University School of Graduate Studies / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=case1386188714.

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28

West, Joseph. "Self-play deep learning for games: Maximising experiences." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/204194/1/Joseph_West_Thesis.pdf.

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This thesis describes several studies focused on improving the learning efficiency to train a combined tree-search/neural-network reinforcement learning agent for different board games. The work's primary contribution is a new approach to creating training experiences by enforcing a structured learning paradigm called the end-game-first curriculum which is shown to improve the speed of learning when compared against the current state-of-the-art agent. The thesis identifies a bottleneck in the self-play experience generation for a reinforcement learning agent and explores different methods to m
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Ishida, Shoichi. "Development of an AI-Driven Organic Synthesis Planning Approach with Retrosynthesis Knowledge." Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263605.

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Prince, Jared. "Exploring the Effect of Different Numbers of Convolutional Filters and Training Loops on the Performance of AlphaZero." TopSCHOLAR®, 2018. https://digitalcommons.wku.edu/theses/3087.

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In this work, the algorithm used by AlphaZero is adapted for dots and boxes, a two-player game. This algorithm is explored using different numbers of convolutional filters and training loops, in order to better understand the effect these parameters have on the learning of the player. Different board sizes are also tested to compare these parameters in relation to game complexity. AlphaZero originated as a Go player using an algorithm which combines Monte Carlo tree search and convolutional neural networks. This novel approach, integrating a reinforcement learning method previously applied to
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31

Buron, Cédric. "Un système multi-agents pour une place de marché de facture." Electronic Thesis or Diss., Sorbonne université, 2018. http://www.theses.fr/2018SORUS314.

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Dans les entreprises, l’écart entre la date où les entreprises engagent leurs dépenses et la date où elles sont payées entraîne un besoin en fonds de roulement à l’origine de problèmes dont la sévérité va de la perte de contrat au dépôt de bilan. Dans le contexte actuel, le recours à l’emprunt pour éviter ces situations est hélas complexe pour les PME, les banques refusant de prêter lorsqu’elles estiment que le risque est trop important. Une possibilité est alors de recourir au factoring, et de faire financer ses créances. Dans cette thèse, nous proposons une place de marché de factoring intel
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Aguiar, Matheus Araújo. "Um agente jogador de GO com busca em árvore Monte-Carlo aprimorada por memória esparsamente distribuída." Universidade Federal de Uberlândia, 2013. https://repositorio.ufu.br/handle/123456789/12547.

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The game of Go is very ancient, with more than 4000 years of history and it is still popular nowadays, representing a big challenge to the Articial Intelligence. Despite its simple rules, the techniques which obtained success in other games like chess and draughts cannot handle satisfactorily the complex patterns and behaviours that emerge during a match of Go. The present work implements the SDM-Go, a competitive agent for Go that seeks to reduce the usage of supervision in the search process for the best move. The SDMGo utilizes the sparse distributed memory model as an additional resource t
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Žlebek, Petr. "Hra Sokoban a umělá inteligence." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-442845.

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The thesis is focused on solving the Sokoban game using artificial intelligence algorithms. The first part of the thesis describes the Sokoban game, state space and selected state space search methods. In the second part selected methods were implemented and graphic user interface was created in the Python environment. Comparative experiments were executed in the final part.
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Wang, Weijia. "Multi-objective sequential decision making." Phd thesis, Université Paris Sud - Paris XI, 2014. http://tel.archives-ouvertes.fr/tel-01057079.

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This thesis is concerned with multi-objective sequential decision making (MOSDM). The motivation is twofold. On the one hand, many decision problems in the domains of e.g., robotics, scheduling or games, involve the optimization of sequences of decisions. On the other hand, many real-world applications are most naturally formulated in terms of multi-objective optimization (MOO). The proposed approach extends the well-known Monte-Carlo tree search (MCTS) framework to the MOO setting, with the goal of discovering several optimal sequences of decisions through growing a single search tree. The ma
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Trutman, Michal. "Agentní systém pro hraní her." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-234964.

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This thesis deals with general game playing agent systems. On the contrary with common agents, which are designed only for a specified task or a game, general game playing agents have to be able to play basically any arbitrary game described in a formal declarative language. The biggest challenge is that the game rules are not known beforehand, which makes it impossible to use some optimizations or to make a good heuristic function. The thesis consists of a theoretical and a practical part. The first part introduces the field of general game playing agents, defines the Game Description Languag
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Michelucci, Romain. "Recherche arborescente Monte Carlo et évolution artificielle pour l'identification de paramètres dynamiques dans les réseaux de régulation génétiques hybrides." Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ4060.

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La modélisation des systèmes biologiques a pour but d'aider à la compréhension des systèmes biologiques via une représentation de la dynamique de ces systèmes qui peut, elle-même, être étudiée de manière informatique. Le goulot d'étranglement de la démarche de modélisation est la recherche de paramétrisations de modèles qui sont en accord avec les connaissances biologiques disponibles. Ici, nous adressons ce problème dans le cadre de la modélisation hybride des réseaux de régulation génétiques. Dans cette thèse, nous commençons par formuler le problème en un problème d'optimisation afin d'exhi
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Hoock, Jean-Baptiste. "Contributions to Simulation-based High-dimensional Sequential Decision Making." Phd thesis, Université Paris Sud - Paris XI, 2013. http://tel.archives-ouvertes.fr/tel-00912338.

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My thesis is entitled "Contributions to Simulation-based High-dimensional Sequential Decision Making". The context of the thesis is about games, planning and Markov Decision Processes. An agent interacts with its environment by successively making decisions. The agent starts from an initial state until a final state in which the agent can not make decision anymore. At each timestep, the agent receives an observation of the state of the environment. From this observation and its knowledge, the agent makes a decision which modifies the state of the environment. Then, the agent receives a reward
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Paumard, Marie-Morgane. "Résolution automatique de puzzles par apprentissage profond." Thesis, CY Cergy Paris Université, 2020. http://www.theses.fr/2020CYUN1067.

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L’objectif de cette thèse est de développer des méthodes sémantiques de réassemblage dans le cadre compliqué des collections patrimoniales, où certains blocs sont érodés ou manquants.Le remontage de vestiges archéologiques est une tâche importante pour les sciences du patrimoine : il permet d’améliorer la compréhension et la conservation des vestiges et artefacts anciens. Certains ensembles de fragments ne peuvent être réassemblés grâce aux techniques utilisant les informations de contour et les continuités visuelles. Il est alors nécessaire d’extraire les informations sémantiques des fragment
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39

Li, Shuying. "Phylogenetic tree construction using markov chain monte carlo /." The Ohio State University, 1996. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487942182323916.

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40

Sun, Xihao. "Pricing Options with Monte Carlo and Binomial Tree Methods." Digital WPI, 2011. https://digitalcommons.wpi.edu/etd-theses/687.

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This report describes our work in pricing options using computational methods. First, I collected the historical asset prices for assets in four economic sectors to estimate model parameters, such as asset returns and covariances. Then I used these parameters to model asset prices using multiple geometric Brownian motion and simulate new asset prices. Using the generated prices, I used Monte Carlo methods and control variates to price call options. Next I used the binomial tree model to price put options, which I was introduced to in the course Math 571: Financial Mathematics I. Using the esti
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41

Bertolino, Mattias, and Arvi Jonnarth. "Monte Carlo methods applied to tree-structured decision processes." Thesis, Uppsala universitet, Tillämpad matematik och statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-325830.

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42

Mathonat, Romain. "Rule discovery in labeled sequential data : Application to game analytics." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEI080.

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Exploiter des jeux de données labelisés est très utile, non seulement pour entrainer des modèles et mettre en place des procédures d'analyses prédictives, mais aussi pour améliorer la compréhension d'un domaine. La découverte de sous-groupes a été l'objet de recherches depuis deux décennies. Elle consiste en la découverte de règles couvrants des ensembles d'objets ayant des propriétés intéressantes, qui caractérisent une classe cible donnée. Bien que de nombreux algorithmes de découverte de sous-groupes aient été proposés à la fois dans le cas des données transactionnelles et numériques, la dé
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43

Holmes, Michael P. "Multi-tree Monte Carlo methods for fast, scalable machine learning." Diss., Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/33865.

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As modern applications of machine learning and data mining are forced to deal with ever more massive quantities of data, practitioners quickly run into difficulty with the scalability of even the most basic and fundamental methods. We propose to provide scalability through a marriage between classical, empirical-style Monte Carlo approximation and deterministic multi-tree techniques. This union entails a critical compromise: losing determinism in order to gain speed. In the face of large-scale data, such a compromise is arguably often not only the right but the only choice. We refer to this ne
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Cheon, Sooyoung. "Protein folding and phylogenetic tree reconstruction using stochastic approximation Monte Carlo." Texas A&M University, 2003. http://hdl.handle.net/1969.1/5785.

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Recently, the stochastic approximation Monte Carlo algorithm has been proposed by Liang et al. (2005) as a general-purpose stochastic optimization and simulation algorithm. An annealing version of this algorithm was developed for real small protein folding problems. The numerical results indicate that it outperforms simulated annealing and conventional Monte Carlo algorithms as a stochastic optimization algorithm. We also propose one method for the use of secondary structures in protein folding. The predicted protein structures are rather close to the true structures. Phylogenetic trees have b
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Luczynska, Marta Magdalena. "Markov chain Monte Carlo and its applications to phylogenetic tree construction." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/62989.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 93-96).<br>This thesis addresses the application of Bayesian methods to problems in phylogenetics. Specifically, we focus on using genetic data to estimate phylogenetic trees representing the evolutionary history of genes and species. Knowledge of this common ancestry has implications for the identification of functions and properties of genes, the effect of mutations and their roles in particula
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46

Cornu, Marek. "Local Search, data structures and Monte Carlo Search for Multi-Objective Combinatorial Optimization Problems." Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLED043/document.

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De nombreux problèmes d'optimisation combinatoire considèrent plusieurs objectifs, souvent conflictuels. Cette thèse s'intéresse à l'utilisation de méthodes de recherche locale, de structures de données et de recherche Monte-Carlo pour la recherche de l'ensemble des solutions efficaces de tels problèmes, représentant l'ensemble des meilleurs compromis pouvant être réalisés en considération de tous les objectifs.Nous proposons une nouvelle méthode d'approximation appelée 2-Phase Iterated Pareto Local Search based on Decomposition (2PIPLS/D) combinant les concepts de recherche locale Pareto (PLS
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Wolf, Travis Benjamin. "Aircraft collision avoidance using Monte Carlo Real-Time Belief Space Search." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/54226.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2009.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student submitted PDF version of thesis.<br>Includes bibliographical references (p. 93-95).<br>This thesis presents the Monte Carlo Real-Time Belief Space Search (MC-RTBSS) algorithm, a novel, online planning algorithm for partially observable Markov decision processes (POMDPs). MC-RTBSS combines a sample-based belief state represe
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Arvestad, Isaac, and Henrik Lagebrand. "Implementing Bayesian phylogenetic tree inference with Sequential Monte Carlo and the Phylogenetic Likelihood Library." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229429.

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We investigate the usability of the Phylogenetic Likelihood Library (PLL) in Bayesian phylogenetic tree inference using Sequential Monte Carlo (SMC) algorithms. This is done by implementing two different versions of the same algorithm with two different approaches of the use of PLL. The implementation using the main PLL API encountered performance issues that the lower level implementation did not. We conclude that it is possible to use PLL in SMC methods but it is unclear if the main API is suitable.<br>Vi undersöker om programspråksbiblioteket Phylogenetic Likelihood Library (PLL) kan använd
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Walker, Neil Rawlinson. "A Bayesian approach to the job search model and its application to unemployment durations using MCMC methods." Thesis, University of Newcastle Upon Tyne, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.299053.

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Glaude, Robin Francoise. "Applicability of Uncertainty analysis to groundwater environmental risks through Fault Tree Analysis and Monte Carlo simulations." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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The Anthropocene epoch initiated by human in uence on its Earth system (biosphere, hydrosphere, ...) leads to an irreversible change: Global warming. Climate change alters all existing natural processes, including the ones related to groundwater. The present paper aims to study the occurrence's probability of two particular groundwater risks: the generation of thermokarst lakes in permafrost environment and its subsequent thermal consequences in the surroundings as well as seawater intrusion inducing saltwater contamination in pumping wells. These processes are dependent of physical pa
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