The Upper Confidence bounds for Trees ( UCT ) algorithm has in recent years captured the attention of the planning and game-playing community due to its notable success in the game of Go . However , attempts to reproduce similar levels of performance in domains that are the forte of Minimax-style algorithms have been largely unsuccessful , making any comparative studies of the two hard . However , DCA is effective on only some proteins with a very large number of sequence homologs . To further improve contact prediction , we borrow ideas from deep learning , which has recently revolutionized object recognition , speech recognition and the GO game . Our deep learning method can model complex sequence-structure relationship and high-order correlation ( i.e. , contact occurring patterns ) and thus , improve contact prediction accuracy greatly . Recent progress in machine learning techniques have revived interest in building artificial general intelligence using these particular tools . There has been a tremendous success in applying them for narrow intellectual tasks such as pattern recognition , natural language processing and playing Go . The latter application vastly outperforms the strongest human player in recent years . Contextual patterns that appear jointly in local contexts may form a pattern collocation , with its significance measured by hypothesis testing . Our experiments show that GO-playing knowledge acquired in the form of contextual patterns and pattern collocations is effective in move generation at various stages of GO-playing . Abstract The quality , quantity , and consistency of the knowledge used in GO-playing programs often determine their strengths , and automatic acquisition of large amounts of high-quality and consistent GO knowledge is crucial for successful GO playing . In a previous article of this subject , we have presented an algorithm for efficient and automatic acquisition of spatial patterns of GO as well as their frequency of occurrence from game records . A rich source of big data is game data . In this article , we focus on the board game of Go , which is a popular two-player strategic board game . Due to its popularity , many people are studying sequences of moves in games ( i.e. , joseki ) . Analysts can then examine such fused data with a combination of visualizations . We demonstrate our methods for the competitive game Go , which is especially complex for the analysis of strategies of individual players . As a typical example of dynamic search algorithm , the UCT algorithm was initially used on the computerized game of GO . This paper briefly introduces the Markov Decision process , the Multi-armed Bandit model , and the Upper-Confidence Bandit formula . In this paper , we present a robotic prediction agent including a darkforest Go engine , a fuzzy markup language ( FML ) assessment engine , an FML-based decision support engine , and a robot engine for game of Go application . The knowledge base and rule base of FML assessment engine are constructed by referring the information from the darkforest Go engine located in NUTN and OPU , for example , the number of MCTS simulations and winning rate prediction . Computer programs for the game of Go play only at the level of an advanced beginning player . The standard approach to constructing a program based on brute force game-tree search does not work well because of the game tree size and , more significantly , the difficulty in constructing fast , accurate heuristic evaluation functions . This paper claims that time is ripe for computer-aided Go on a large scale , although neither most users nor the Go programmers have realized it . A central part of the paper describes successful pioneers in Go play with computer help . Progress in computer-aided Go may also lead to progress in human Go and in computer Go itself . The game of chess is a complex game with rules and steps that a full calculation . One of the algo- rithms are quite eciently as Arti cial Intelligence in the game of chess is the Minimax algorithm . This paper is an immediate follow-up of the author 's previous “ From Cohomology in Physics to q-connectivity in Social Science ” , published in the previous issue of this journal . It is an illustration of the theory of structure contained in the first paper , being an application to the game of chess . The relations which exist between the chess-men and the squares of the chessboard , together with the rules of the game , are used to define mathematical relations ΓW and ΓB . It becomes so whenever the system , through lack of knowledge , is faced with a choice from a number of alternatives , where each choice leads to the need to make further choices , and so on until the problem is solved . Playing chess is a classic example of this situation . Other examples include the attempt to diagnose a malfunction in some complex piece of machinery or determining how best to cut material to make an item of clothing with the minimum of waste . Artificial intelligence is a product of applied equipment and software that copies many human behaviors , such as reasoning , learning , problem solving , and knowledge . Artificial intelligence can be used in areas such as learning , playing chess , proving mathematical theorems , writing poetry , and diagnosing diseases . To explain the applications of artificial intelligence , some concepts must be brought to the debate . The article develops the decision rules to win each set of the Chinese chess game using artificial intelligent and evaluation algorithm , and presents the movement scenarios of the chesses using mobile robots on the grid based platform . Artificial intelligent is often applied in computer chess game , and programs the movement method to win each set for a player ( red side or black side ) . We play the Chinese chess game using the mouse according to the game rules on the user interface . Computer Chess game is an important research field of artificial intelligence , and amazon chess is a kind of chess with complicated strategies . In this paper the author designed an amazon chess game system contains the description of the situation , the method to generate and strategy - the value evaluation . This project uses Alpha-Beta pruning search to access match pieces , and then decide which piece to move . Window-Based Alpha-Beta Pruning Search Algorithm All the computer game of chess involves searching algorithm . It ’s used to find the optimum path in game tree according to present situation and the set searching depth and width . So far , most of the existing EMS strategies are either just simply following predefined rules that are not adaptive to changing driving conditions ; or heavily relying on accurate prediction of future traffic conditions . Deep learning algorithms have been successfully applied to many complex problems and proved to even outperform human beings in some tasks ( e.g. , play chess ) in recent years , which shows the great potential of such methods in practical engineering problems . In this study , a deep reinforcement learning ( Deep Q-network or DQN ) based PHEV energy management system is designed to autonomously learn the optimal fuel/electricity splits from interactions between the vehicle and the traffic environment . Classroom efforts such as Groen ’s lab course in sensor integration [ GdBvIS92 ] are an early example . More recently , the Association for Advancement of Artificial Intelligence ( AAAI ) organized a small scale manipulation challenge with playing of chess as the competition task1 . Today , many chess playing robots exist in various forms ranging from humanoids to industrial grade manipulators . This paper describes our video-based gesture recognition system that observes the user in front of a large displaying screen , identifying three different hand gestures in real time using 2D feature classification and determines 3D information like the 3D position of the user 's hand or the pointing direction if performed . Different scenario applications like a virtual chess game against the computer or an industrial scenario have been developed and tested . To estimate the possible count of distinguishable gestures a sign language recognition application has been developed and tested using a single uncalibrated camera only . War chess gaming has so far received insufficient attention but is a significant component of turn-based strategy games TBS and is studied in this paper . First , a common game model is proposed through various existing war chess types . Naturally , many shorter words can be traced in the square-BEND , GIRT , and LIAR , for instance-but these shorter words are too easy to find , so that we have restricted the problem to words of at least five letters . There are a number of conditions governing word chess . For one thing , any given letter square may not be used more than once for one · word . document : Mastering Chess and Shogi by Self - Play with a General Reinforcement Learning Algorithm The game of chess is the most widely - studied domain in the history of artificial intelligence . The strongest programs are based on a combination of sophisticated search techniques , domain - specific adaptations , and handcrafted evaluation functions that have been refined by human experts over several decades . Starting from random play , and given no domain knowledge except the game rules , AlphaZero achieved within 24 hours a superhuman level of play in the games of chess and shogi ( Japanese chess ) as well as Go , and convincingly defeated a world - champion program in each case . The study of computer chess is as old as computer science itself . Babbage , Turing , Shannon , and von Neumann devised hardware , algorithms and theory to analyse and play the game of chess . Chess subsequently became the grand challenge task for a generation of artificial intelligence researchers , culminating in high - performance computer chess programs that perform at superhuman level . However , these systems are highly tuned to their domain , and can not be generalised to other problems without significant human effort . A landmark for artificial intelligence was achieved in 1997 when Deep Blue defeated the human world champion . Computer chess programs continued to progress steadily beyond human level in the following two decades . These programs evaluate positions using features handcrafted by human grandmasters and carefully tuned weights , combined with a high - performance alpha - beta search that expands a vast search tree using a large number of clever heuristics and domain - specific adaptations . It is likely that some of these techniques could further improve the performance of AlphaZero ; however , we have focused on a pure self - play reinforcement learning approach and leave these extensions for future research . subsection : Prior Work on Computer Chess and Shogi In this section we discuss some notable prior work on reinforcement learning in computer chess . The Real Meaning of the Sacrifice Move in Board Games . This is a case study of one student ’s research about the Nash equilibrium in the context of board games . First set the stage , i.e. describe the educational environment . The author discusses the use of board games in the teaching of English as a foreign language to technical higher education establishment students , shows the specificity of this type of game , pays special attention to the functions that board games perform in the process of foreign language teaching , the selection criteria and the requirements for their use in classes by a teacher , and gives the description of the stages of board game-playing . In this paper , we propose a novel model that labels a sequence of fragments in jumping order . Specifically , we devise a dedicated boardgame to develop a correspondence between solving STC and board-game playing . By defining proper game rules and devising a game state evaluator in which context clues are injected , at each round , each player is effectively pushed to find the optimal move without position restrictions via considering the current game state , which corresponds to producing a label for an unlabeled fragment jumpily with the consideration of the contexts clues . One key motivation for this proposal stems from a paradox about intuition and intelligence . For the purposes of this paper , it will be useful to consider playing board games — such as chess and Go — as a paradigm of problem solving and cognition , and computer programs as a model of human cognition . I first describe the basic components of computer programs that play board games , namely value functions and search functions . This thesis focuses on the application of the tangible user interface and spatial augmented reality principals for playing board games . For this purpose , a system called „ interactive table “ was developed . Board-game applications are frequently found in mobile devices where the computing performance and the energy budget are constrained . Since the Artificial Intelligence techniques applied in these g ... Board-game applications are frequently found in mobile devices where the computing performance and the energy budget are constrained . Since the Artificial Intelligence techniques applied in these games are computationally intensive , the applications developed for mobile systems are frequently simplistic , far from the level of equivalent applications developed for desktop computers . Monte Carlo Tree Search ( MCTS ) is an effective decision making algorithm that often works well without domain knowledge , finding an increasing application in commercial mobile and video games . A promising application of MCTS is creating AI opponents for board and card games , where Information Set MCTS ( ISMCTS ) can provide a challenging opponent and reduces the cost of creating game-specific AI opponents . Most research to date has aimed at improving the quality of decision making by (IS)MCTS , with respect to time usage . In this way , the sets of constant values ( moving towards winning ) are produced sequentially . This observation motivates our interest in what class of board game problems does the computation of starting points of constant value work efficiently . Furthermore , certain situations may arise repeatedly during the play ; this corresponds to a problem where some values on the right-hand side of Bellman 's DP optimality equation are unknown when the specific value function on the left-hand side is evaluated . This research tries to find the better method in processing the potential of traditional board games , which will later become a map in the research development . This studies aims to find the research stage by applying the model “ From SAD to CHEER ” in the context of traditional board games studies . Dual λ-search can handle inversions efficiently compared to previous algorithms by making passes for both players . We implemented dual λ-search with Df-pn as the driver , and made experiments with difficult Shogi-endgame problems . We showed the effectiveness of our algorithm by solving 32 problems out of 97 . These include the depth of search appropriate for successful learning and the use of self-play to enable the algorithm to be independent of human knowledge . We then report on experiments that obtained values for use in shogi-playing programs . Unlike chess , shogi has no generally agreed standardized set of values for pieces , so there is more need for machine learning . document : Mastering Chess and Shogi by Self - Play with a General Reinforcement Learning Algorithm The game of chess is the most widely - studied domain in the history of artificial intelligence . In this paper , we generalise this approach into a single AlphaZero algorithm that can achieve , tabula rasa , superhuman performance in many challenging domains . Starting from random play , and given no domain knowledge except the game rules , AlphaZero achieved within 24 hours a superhuman level of play in the games of chess and shogi ( Japanese chess ) as well as Go , and convincingly defeated a world - champion program in each case . The study of computer chess is as old as computer science itself .
The Upper Confidence bounds for Trees ( UCT ) algorithm has in recent years captured the attention of the planning and game-playing community due to its notable success in the game of Go . However , attempts to reproduce similar levels of performance in domains that are the forte of Minimax-style algorithms have been largely unsuccessful , making any comparative studies of the two hard .
However , DCA is effective on only some proteins with a very large number of sequence homologs . To further improve contact prediction , we borrow ideas from deep learning , which has recently revolutionized object recognition , speech recognition and the GO game . Our deep learning method can model complex sequence-structure relationship and high-order correlation ( i.e. , contact occurring patterns ) and thus , improve contact prediction accuracy greatly .
Recent progress in machine learning techniques have revived interest in building artificial general intelligence using these particular tools . There has been a tremendous success in applying them for narrow intellectual tasks such as pattern recognition , natural language processing and playing Go . The latter application vastly outperforms the strongest human player in recent years .
Contextual patterns that appear jointly in local contexts may form a pattern collocation , with its significance measured by hypothesis testing . Our experiments show that GO-playing knowledge acquired in the form of contextual patterns and pattern collocations is effective in move generation at various stages of GO-playing .
Abstract The quality , quantity , and consistency of the knowledge used in GO-playing programs often determine their strengths , and automatic acquisition of large amounts of high-quality and consistent GO knowledge is crucial for successful GO playing . In a previous article of this subject , we have presented an algorithm for efficient and automatic acquisition of spatial patterns of GO as well as their frequency of occurrence from game records .
A rich source of big data is game data . In this article , we focus on the board game of Go , which is a popular two-player strategic board game . Due to its popularity , many people are studying sequences of moves in games ( i.e. , joseki ) .
Analysts can then examine such fused data with a combination of visualizations . We demonstrate our methods for the competitive game Go , which is especially complex for the analysis of strategies of individual players .
As a typical example of dynamic search algorithm , the UCT algorithm was initially used on the computerized game of GO . This paper briefly introduces the Markov Decision process , the Multi-armed Bandit model , and the Upper-Confidence Bandit formula .
In this paper , we present a robotic prediction agent including a darkforest Go engine , a fuzzy markup language ( FML ) assessment engine , an FML-based decision support engine , and a robot engine for game of Go application . The knowledge base and rule base of FML assessment engine are constructed by referring the information from the darkforest Go engine located in NUTN and OPU , for example , the number of MCTS simulations and winning rate prediction .
Computer programs for the game of Go play only at the level of an advanced beginning player . The standard approach to constructing a program based on brute force game-tree search does not work well because of the game tree size and , more significantly , the difficulty in constructing fast , accurate heuristic evaluation functions .
This paper claims that time is ripe for computer-aided Go on a large scale , although neither most users nor the Go programmers have realized it . A central part of the paper describes successful pioneers in Go play with computer help . Progress in computer-aided Go may also lead to progress in human Go and in computer Go itself .
The game of chess is a complex game with rules and steps that a full calculation . One of the algo- rithms are quite eciently as Arti cial Intelligence in the game of chess is the Minimax algorithm .
This paper is an immediate follow-up of the author 's previous “ From Cohomology in Physics to q-connectivity in Social Science ” , published in the previous issue of this journal . It is an illustration of the theory of structure contained in the first paper , being an application to the game of chess . The relations which exist between the chess-men and the squares of the chessboard , together with the rules of the game , are used to define mathematical relations ΓW and ΓB .
It becomes so whenever the system , through lack of knowledge , is faced with a choice from a number of alternatives , where each choice leads to the need to make further choices , and so on until the problem is solved . Playing chess is a classic example of this situation . Other examples include the attempt to diagnose a malfunction in some complex piece of machinery or determining how best to cut material to make an item of clothing with the minimum of waste .
Artificial intelligence is a product of applied equipment and software that copies many human behaviors , such as reasoning , learning , problem solving , and knowledge . Artificial intelligence can be used in areas such as learning , playing chess , proving mathematical theorems , writing poetry , and diagnosing diseases . To explain the applications of artificial intelligence , some concepts must be brought to the debate .
The article develops the decision rules to win each set of the Chinese chess game using artificial intelligent and evaluation algorithm , and presents the movement scenarios of the chesses using mobile robots on the grid based platform . Artificial intelligent is often applied in computer chess game , and programs the movement method to win each set for a player ( red side or black side ) . We play the Chinese chess game using the mouse according to the game rules on the user interface .
Computer Chess game is an important research field of artificial intelligence , and amazon chess is a kind of chess with complicated strategies . In this paper the author designed an amazon chess game system contains the description of the situation , the method to generate and strategy - the value evaluation .
This project uses Alpha-Beta pruning search to access match pieces , and then decide which piece to move . Window-Based Alpha-Beta Pruning Search Algorithm All the computer game of chess involves searching algorithm . It ’s used to find the optimum path in game tree according to present situation and the set searching depth and width .
So far , most of the existing EMS strategies are either just simply following predefined rules that are not adaptive to changing driving conditions ; or heavily relying on accurate prediction of future traffic conditions . Deep learning algorithms have been successfully applied to many complex problems and proved to even outperform human beings in some tasks ( e.g. , play chess ) in recent years , which shows the great potential of such methods in practical engineering problems . In this study , a deep reinforcement learning ( Deep Q-network or DQN ) based PHEV energy management system is designed to autonomously learn the optimal fuel/electricity splits from interactions between the vehicle and the traffic environment .
Classroom efforts such as Groen ’s lab course in sensor integration [ GdBvIS92 ] are an early example . More recently , the Association for Advancement of Artificial Intelligence ( AAAI ) organized a small scale manipulation challenge with playing of chess as the competition task1 . Today , many chess playing robots exist in various forms ranging from humanoids to industrial grade manipulators .
This paper describes our video-based gesture recognition system that observes the user in front of a large displaying screen , identifying three different hand gestures in real time using 2D feature classification and determines 3D information like the 3D position of the user 's hand or the pointing direction if performed . Different scenario applications like a virtual chess game against the computer or an industrial scenario have been developed and tested . To estimate the possible count of distinguishable gestures a sign language recognition application has been developed and tested using a single uncalibrated camera only .
War chess gaming has so far received insufficient attention but is a significant component of turn-based strategy games TBS and is studied in this paper . First , a common game model is proposed through various existing war chess types .
Naturally , many shorter words can be traced in the square-BEND , GIRT , and LIAR , for instance-but these shorter words are too easy to find , so that we have restricted the problem to words of at least five letters . There are a number of conditions governing word chess . For one thing , any given letter square may not be used more than once for one · word .
document : Mastering Chess and Shogi by Self - Play with a General Reinforcement Learning Algorithm The game of chess is the most widely - studied domain in the history of artificial intelligence . The strongest programs are based on a combination of sophisticated search techniques , domain - specific adaptations , and handcrafted evaluation functions that have been refined by human experts over several decades .
Starting from random play , and given no domain knowledge except the game rules , AlphaZero achieved within 24 hours a superhuman level of play in the games of chess and shogi ( Japanese chess ) as well as Go , and convincingly defeated a world - champion program in each case . The study of computer chess is as old as computer science itself . Babbage , Turing , Shannon , and von Neumann devised hardware , algorithms and theory to analyse and play the game of chess . Chess subsequently became the grand challenge task for a generation of artificial intelligence researchers , culminating in high - performance computer chess programs that perform at superhuman level . However , these systems are highly tuned to their domain , and can not be generalised to other problems without significant human effort .
A landmark for artificial intelligence was achieved in 1997 when Deep Blue defeated the human world champion . Computer chess programs continued to progress steadily beyond human level in the following two decades . These programs evaluate positions using features handcrafted by human grandmasters and carefully tuned weights , combined with a high - performance alpha - beta search that expands a vast search tree using a large number of clever heuristics and domain - specific adaptations .
It is likely that some of these techniques could further improve the performance of AlphaZero ; however , we have focused on a pure self - play reinforcement learning approach and leave these extensions for future research . subsection : Prior Work on Computer Chess and Shogi In this section we discuss some notable prior work on reinforcement learning in computer chess .
The Real Meaning of the Sacrifice Move in Board Games . This is a case study of one student ’s research about the Nash equilibrium in the context of board games . First set the stage , i.e. describe the educational environment .
The author discusses the use of board games in the teaching of English as a foreign language to technical higher education establishment students , shows the specificity of this type of game , pays special attention to the functions that board games perform in the process of foreign language teaching , the selection criteria and the requirements for their use in classes by a teacher , and gives the description of the stages of board game-playing .
In this paper , we propose a novel model that labels a sequence of fragments in jumping order . Specifically , we devise a dedicated boardgame to develop a correspondence between solving STC and board-game playing . By defining proper game rules and devising a game state evaluator in which context clues are injected , at each round , each player is effectively pushed to find the optimal move without position restrictions via considering the current game state , which corresponds to producing a label for an unlabeled fragment jumpily with the consideration of the contexts clues .
One key motivation for this proposal stems from a paradox about intuition and intelligence . For the purposes of this paper , it will be useful to consider playing board games — such as chess and Go — as a paradigm of problem solving and cognition , and computer programs as a model of human cognition . I first describe the basic components of computer programs that play board games , namely value functions and search functions .
This thesis focuses on the application of the tangible user interface and spatial augmented reality principals for playing board games . For this purpose , a system called „ interactive table “ was developed .
Board-game applications are frequently found in mobile devices where the computing performance and the energy budget are constrained . Since the Artificial Intelligence techniques applied in these g ...
Board-game applications are frequently found in mobile devices where the computing performance and the energy budget are constrained . Since the Artificial Intelligence techniques applied in these games are computationally intensive , the applications developed for mobile systems are frequently simplistic , far from the level of equivalent applications developed for desktop computers .
Monte Carlo Tree Search ( MCTS ) is an effective decision making algorithm that often works well without domain knowledge , finding an increasing application in commercial mobile and video games . A promising application of MCTS is creating AI opponents for board and card games , where Information Set MCTS ( ISMCTS ) can provide a challenging opponent and reduces the cost of creating game-specific AI opponents . Most research to date has aimed at improving the quality of decision making by (IS)MCTS , with respect to time usage .
In this way , the sets of constant values ( moving towards winning ) are produced sequentially . This observation motivates our interest in what class of board game problems does the computation of starting points of constant value work efficiently . Furthermore , certain situations may arise repeatedly during the play ; this corresponds to a problem where some values on the right-hand side of Bellman 's DP optimality equation are unknown when the specific value function on the left-hand side is evaluated .
This research tries to find the better method in processing the potential of traditional board games , which will later become a map in the research development . This studies aims to find the research stage by applying the model “ From SAD to CHEER ” in the context of traditional board games studies .
Dual λ-search can handle inversions efficiently compared to previous algorithms by making passes for both players . We implemented dual λ-search with Df-pn as the driver , and made experiments with difficult Shogi-endgame problems . We showed the effectiveness of our algorithm by solving 32 problems out of 97 .
These include the depth of search appropriate for successful learning and the use of self-play to enable the algorithm to be independent of human knowledge . We then report on experiments that obtained values for use in shogi-playing programs . Unlike chess , shogi has no generally agreed standardized set of values for pieces , so there is more need for machine learning .
document : Mastering Chess and Shogi by Self - Play with a General Reinforcement Learning Algorithm The game of chess is the most widely - studied domain in the history of artificial intelligence .
In this paper , we generalise this approach into a single AlphaZero algorithm that can achieve , tabula rasa , superhuman performance in many challenging domains . Starting from random play , and given no domain knowledge except the game rules , AlphaZero achieved within 24 hours a superhuman level of play in the games of chess and shogi ( Japanese chess ) as well as Go , and convincingly defeated a world - champion program in each case . The study of computer chess is as old as computer science itself .
Trade-Offs in Sampling-Based Adversarial Planning Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model On modelling the emergence of logical thinking Contextual patterns and pattern collocations in the game of GO Automatic acquisition of pattern collocations in GO Data analytics on the board game Go for the discovery of interesting sequences of moves in joseki Demo of a Visual Gaze Analysis System for Virtual Board Games Technique analysis and designing of program with UCT algorithm for NoGo FML-based prediction agent and its application to game of Go Towards Incorporating Intent Inference into the Game of Go Computer-Aided Go: Chess as a Role Model MINIMAX ALPHABETA ALGORITHM IMPLEMENTATION ON PRUNING CHESS ONLINE GAME USING LUA, LUAGAME, AND MODULE SOCKET LUA Multi-dimensional structure in the game of chess Search-Related Techniques in AI Accounting & Management under the Influences of AI Implementation of the chess game artificial intelligent using mobile robots Design of Amazon Chess Game System Based on Reinforcement Learning The Design and Realization of Connect-six Computer Game Software Deep reinforcement learning enabled self-learning control for energy efficient driving Development of a Cheap Chess Robot: Planning and Perception Gestural Interaction Using Feature Classification Turn-Based War Chess Model and Its Search Algorithm per Turn Word Chess With a Vengeance Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm An Application of Nash Equilibrium to an Experimental Setting: The Real Meaning of the Sacrifice Move in Board Games Использование настольных игр при обучении английскому языку как иностранному студентов технического вуза Solving Sequential Text Classification as Board-Game Playing Intuition, intelligence, data compression Deskové hry na interaktivním stole Performance and energy efficiency analysis of a Reversi player for FPGAs and General Purpose Processors Performance and energy efficiency analysis of a Reversi player for FPGAs and General Purpose Processors Memory Bounded Monte Carlo Tree Search On a staged successive approximation procedure for approximate dynamic programming solutions to board game playing Research Model for Traditional Board Game Renaissance Dual Lambda Search and Shogi Endgames Temporal difference learning applied to game playing and the results of application to Shogi Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
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2011 2016 2019 2011 2008 2018 2020 2013 2017 2002 2016 2011 1972 1992 2015 2014 2019 2015 2019 2012 2008 2016 1968 2017 2018 2013 2020 2019 2016 2015 2015 2017 2015 2018 2006 2001 2017
ICAPS KES ETRA Adjunct IFSA-SCIS Computers and Games SCIS&amp;ISIS Informatiktage AMDO ArXiv AAAI AIIDE IEEM HCI (12) ACG ArXiv
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