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alphazero s methods to chess which is currently among the leading chess playing programs references edit 1 2 silver david huang aja maddison chris j guez arthur sifre laurent driessche george van den schrittwieser julian antonoglou ioannis panneershelvam veda lanctot marc dieleman sander grewe dominik nham john kalchbrenner nal sutskever ilya lillicrap timothy leach madeleine kavukcuoglu koray graepel thore hassabis demis 28 january 2016 mastering the game of go with deep neural networks and tree search nature 529 7587 484 489 bibcode 2016natur 529 484s doi 10 1038 nature16961 issn 0028 0836 pmid 26819042 s2cid 515925 1 2 silver david 2017 mastering chess and shogi by self play with a general reinforcement learning algorithm arxiv 1712 01815v1 cs ai rajkumar prahalad a survey of monte carlo techniques in games pdf cs umd edu archived pdf from the original on 2023 04 07 monte carlo tree search in total war rome ii s campaign ai ai game dev archived from the original on 13 march 2017 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simon m cowling peter i rohlfshagen philipp tavener stephen perez diego samothrakis spyridon colton simon 2012 a survey of monte carlo tree search methods ieee transactions on computational intelligence and ai in games 4 1 1 43 bibcode 2012itcia 4a6810b doi 10 1109 tciaig 2012 2186810 issn 1943 068x althöfer ingo 2012 on board filling games with random turn order and monte carlo perfectness advances in computer games lecture notes in computer science vol 7168 pp 258 269 doi 10 1007 978 3 642 31866 5_22 isbn 978 3 642 31865 8 ramanujan raghuram sabharwal ashish selman bart may 2010 on adversarial search spaces and sampling based planning icaps 10 proceedings of the twentieth international conference on international conference on automated planning and scheduling icaps 10 242 245 ramanujan raghuram selman bart march 2011 trade offs in sampling based adversarial planning proceedings of the international conference on automated planning and scheduling 21 1 202 209 doi 10 1609 icaps v21i1 13472 s2cid 45147586 lee sedol defeats alphago in masterful comeback game 4 go game guru archived from the original on 2016 11 16 retrieved 2017 07 04 drake peter december 2009 the last good reply policy for monte carlo go icga journal 32 4 221 227 doi 10 3233 icg 2009 32404 seth pellegrino peter drake 2010 investigating the effects of playout strength in monte carlo go proceedings of the 2010 international conference on artificial intelligence icai 2010 july 12 15 2010 las vegas nevada usa hamid r arabnia david de la fuente elena b kozerenko josé angel olivas rui chang peter m lamonica raymond a liuzzi ashu m g solo eds csrea press pp 1015 1018 isbn 978 1 60132 148 0 1 2 gelly sylvain silver david 2007 combining online and offline knowledge in uct pdf machine learning proceedings of the twenty fourth international conference icml 2007 corvallis oregon usa june 20 24 2007 zoubin ghahramani ed acm pp 273 280 isbn 978 1 59593 793 3 archived from the original pdf on 2017 08 28 david silver 2009 reinforcement learning and simulation based search in computer go pdf phd thesis university of alberta rémi coulom clop confident local optimization for noisy black box parameter tuning acg 2011 advances in computer games 13 conference tilburg the netherlands november 20 22 guillaume m j b chaslot mark h m winands jaap van den herik 2008 parallel monte carlo tree search pdf computers and games 6th international conference cg 2008 beijing china september 29 october 1 2008 proceedings h jaap van den herik xinhe xu zongmin ma mark h m winands eds springer pp 60 71 isbn 978 3 540 87607 6 cite book cs1 maint multiple names authors list link markus enzenberger martin müller 2010 a lock free multithreaded monte carlo tree search algorithm in jaap van den herik pieter spronck eds advances in computer games 12th international conference acg 2009 pamplona spain may 11 13 2009 revised papers springer pp 14 20 isbn 978 3 642 12992 6 bibliography edit cameron browne edward powley daniel whitehouse simon lucas peter i cowling philipp rohlfshagen stephen tavener diego perez spyridon samothrakis simon colton march 2012 a survey of monte carlo tree search methods ieee transactions on computational intelligence and ai in games 4 1 1 43 bibcode 2012itcia 4a6810b doi 10 1109 tciaig 2012 2186810 s2cid 9316331 maciej świechowski konrad godlewski bartosz sawicki jacek mańdziuk july 2022 monte carlo tree search a review of recent modifications and applications springer nature artificial intelligence review 56 3 497 2562 66 pages arxiv 2103 04931 doi 10 1007 s10462 022 10228 y cite journal cs1 maint multiple names authors list link external links edit beginner s guide to monte carlo tree search v t e graph and tree traversal algorithms search α β pruning a ida lpa sma best first search beam search bidirectional search breadth first search lexicographic parallel b depth first search iterative deepening d fringe search jump point search monte carlo tree search sss shortest path bellman ford dijkstra s floyd warshall johnson s shortest path faster yen s minimum spanning tree borůvka s kruskal s prim s reverse delete list of graph search algorithms v t e artificial intelligence algorithms search a search algorithm beam search beam stack search best first search breadth first search depth first search general problem solver iterative deepening a iterative deepening depth first search uniform cost search game tree search alpha beta pruning expectiminimax minimax monte carlo tree search sss optimization alopex bayesian optimization conjugate gradient method gradient descent hill climbing levenberg marquardt algorithm quadratic unconstrained binary optimization quasi newton method simulated annealing stochastic gradient descent evolutionary computation differential evolution evolutionary multimodal optimization genetic algorithm genetic programming bio inspired methods ant colony optimization algorithms particle swarm optimization automated reasoning backward chaining forward chaining rete algorithm logic dpll algorithm resolution logic walksat statistical inference baum welch algorithm belief propagation expectation maximization algorithm forward backward algorithm kalman filter markov chain monte carlo mcmc viterbi algorithm pathfinding a d dijkstra s algorithm theta motion planning bug algorithm dynamic window approach graphplan probabilistic roadmap rapidly exploring random tree vector field histogram ensemble learning adaboost bootstrap aggregating brownboost gradient boosting logitboost lpboost random forest randomized weighted majority algorithm weighted majority algorithm classification alternating decision tree c4 5 algorithm cn2 algorithm decision tree learning dominance based rough set approach genetic algorithm for rule set production id3 algorithm k nearest neighbors algorithm logic learning machine logistic regression naive bayes classifier relevance vector machine ripper structured knn support vector machine winnow algorithm clustering dbscan hierarchical clustering k means k means clustering k medoids linde buzo gray algorithm lloyd s algorithm optics association learning apriori algorithm eclat algorithm fp growth algorithm dimensionality reduction diffusion map fastica kernel principal component analysis manifold alignment minimum redundancy feature selection non negative matrix factorization principal component analysis sparse pca t distributed stochastic neighbor embedding neural learning almeida pineda recurrent backpropagation backpropagation generec generalized hebbian algorithm growing self organizing map learning vector quantization leabra perceptron quickprop rprop self organizing map wake sleep algorithm reinforcement learning actor critic algorithm constructing skill trees error driven learning policy gradient method prefrontal cortex basal ganglia working memory proximal policy optimization pvlv q learning skill chaining state action reward state action temporal difference learning deep learning pagedattention vattenti...