The US Naval Postgraduate School and University of Washington each independently developed informal security-themed tabletop games . [ d0x3d ! ] is a board game in which players collaborate as white-hat hackers , tasked to retrieve a set of valuable digital assets held by an adversarial network . Control-Alt-Hack is a card game in which three to six players act as white-hat hackers at a security consulting company . Language games are important to create excitement in language lessons . This session reports on a study that aimed to explore the benefits of using board game to teach grammar . The study was carried out among Pre TESL students of UiTM Pahang , Kuantan Campus . Also this method requires a complex logic loop for finding idle periods . In this paper , we present another technique using game-board which is simple to implement and uses native data structures . However , this simplicity comes at a performance cost which has also been analyzed in this paper . Said first information item may include plain text , pictures , frames , video , word-processor data , spread-sheet data , game figures , game elements , text or picture messages , and combinations thereof . Said step of determining the first location of the game element includes the steps of : transmitting , by at least one transmitter located on the game element , at least one signal identifying said game element ; receiving , by at least one sensor ( 41 ) located on the game board , at least one identifying signal ; and determining , by the game board , the first location based on at least one identifying signal . This allows addition of new game elements to a game without actually new elements being bought because modified information presented on said game elements may modify their kind and appearance . The invention provides a board game board having a respectively different unique information for each geographic information is displayed and the position is the geographical information is displayed , and the game customized information , for example area information , tourist information , cultural assets information , restaurants information and the like corresponding to the unique information that can be displayed in the portable terminal of the player to participate in the present invention relates to " in the information of the board the board can be displayed on a mobile terminal board game apparatus and method " . Board game apparatus which can display information on the board , the board according to the invention the mobile terminal board in a built-in claim 1 RF tag to display the geographical information and a built-in respectively different unique information for each position at which the geographical information is displayed game as the second plate and , when the game is in progress player capable of mounting to move with the position of the board , the board tools , and wherein the 1 RF tag and a wireless linkage to leading the the unique information embedded in the claim 1 RF tag RF tag , the wireless transmission of the player tools , the player tool and the second control unit to receive sending the unique information reading from the 2 RF tag , received from the control unit transmits the unique information to a radio transmitter for transmitting over the air It includes a mobile terminal having a wireless receiving unit receiving the unique information that are sent from the player , The sphere is the radio transmitting apparatus and radio linked mobile terminal is characterized in that it displays the customized information corresponding to the unique information on the display unit . The game market is an increasingly large industry . The board-game market , which is the most traditional in the game market , continues to show a steady growth . It is very important for both publishers and players to predict the propensity of users in this huge market and to recommend new games . In many cases , cyber theft happened due to a lack of understanding of how to secure a single piece of private information . In the present paper , we attempt to provide an inexpensive and easy tool for learning cybersecurity through a board game simulation called Security Defense and Dungeons ( sD&D ) . The basic concept is not only to introduce the latest cybersecurity equipment and solutions , but also to provide cybersecurity education for players to raise awareness regarding cybersecurity threats . Mo Gem game is a board game type game that uses patterned board as its playground . Mo Gem game is played by two players in play turns with five pieces of game tools in the form of pebbles , seeds of sapodilla and other grains owned by every player . In this paper , a mobile competition game was developed in the form of a competitive board game for engaging students in learning local cultural . The students were asked to learn with the mobile game by throwing a dice to determine their moves on a tablet computer . Besides the entertainment , this game can train and improve the power thought concentration and agility of PC users . Othello Game is a game boardgame using intelligence arti- cial ( Arti cial Intelligent ) . AI in this game are located on every level-level , among which the level will make random easily placement and may clause Othello placed anywhere , the level is going to nd the greatest value for obtain max- imum results , and level of diculty , will make predictions to the user and will search for the value at the corner of the game board . Hex [ 1 ] is a two player board game which is traditionally played on a rhombic hexagonal pattern ( See Figure ( 1 ) ) . Players are assigned a colour and make moves by putting a token of their colour onto an empty field on the board . AlphaZero has been very successful in many games . Unfortunately , it still consumes a huge amount of computing resources , the majority of which is spent in self-play . This paper proposes using population based training ( PBT ) to help tune hyperparameters dynamically and improve strength during training time . Another significant advantage is that this method requires a single run only , while incurring a small additional time cost , since the time for generating self-play records remains unchanged though the time for optimization is increased following the AlphaZero training algorithm . In our experiments for 9x9 Go , the PBT method is able to achieve a higher win rate for 9x9 Go than the baselines , each with its own hyperparameter configuration and trained individually . For 19x19 Go , with PBT , we are able to obtain improvements in playing strength . Specifically , the PBT agent can obtain up to 74 % win rate against ELF OpenGo , an open-source state-of-the-art AlphaZero program using a neural network of a comparable capacity . This is compared to a saturated non-PBT agent , which achieves a win rate of 47 % against ELF OpenGo under the same circumstances . AlphaZero learns to play go , chess and shogi at a superhuman level through self play given only the rules of the game . This raises the question of whether a similar thing could be done for mathematics -- a MathZero . Most of these algorithms are actually more or less based on human knowledge or thinking ways to model network entities , environments , and rules , and the accuracy and rationality of models are not strong enough to match the changed battlefield . The AlphaZero algorithm generalised in chess and other games provides a new intelligent method to solve complex problems in the military field . Based on the AlphaZero algorithm , this study proposes a method for intelligent deployment of mobile ad-hoc networks with tactical communication node vehicles . In this paper , we mainly focus on the Big-win strategy to improve the performance of AlphaZero without human knowledge . We are pleased to derive some promising results in which our Big-win approach has improved the strength of the 6x6 Othello program with win rate=63 % , lose rate=28 % , and draw rate=9 % comparing to the original AlphaZero approach based on a fair training and playing time conditions . Therefore , we introduce the Two-Phase-Win strategy to combine AlphaZero and miniMax search with alpha-beta pruning for improving AlphaZero ’s strength . It has improved the strength of the AlphaZero approach applied to Connect4 . The results of experiments show that the Two-Phase-Win strategy has 58 % win rate against the AlphaZero approach and does n’t lose any game in a 100-game match . AlphaZero Self-Play Search Enhancements . Recently , AlphaZero has achieved landmark results in deep reinforcement learning , by providing a single self-play architecture that learned three different games at super human level . AlphaZero is a large and complicated system with many parameters , and success requires much compute power and fine-tuning . Reproducing results in other games is a challenge , and many researchers are looking for ways to improve results while reducing computational demands . AlphaZero 's design is purely based on self-play and makes no use of labeled expert data ordomain specific enhancements ; it is designed to learn from scratch . We propose a novel approach to deal with this cold-start problem by employing simple search enhancements at the beginning phase of self-play training , namely Rollout , Rapid Action Value Estimate ( RAVE ) and dynamically weighted combinations of these with the neural network , and Rolling Horizon Evolutionary Algorithms ( RHEA ) . The success of AlphaZero algorithm in chess games provides ideas and methods for other fields . This research designs an intelligent deployment model of wireless ad hoc network node based on the deep reinforcement learning framework of AlphaZero algorithm . Aiming at the difficult problems of setting learning rate and preventing model overfitting in the process of model self-training optimization , this paper chooses gradually decreasing dynamics . The AlphaGo , AlphaGo Zero , and AlphaZero series of algorithms are remarkable demonstrations of deep reinforcement learning 's capabilities , achieving superhuman performance in the complex game of Go with progressively increasing autonomy . However , many obstacles remain in the understanding of and usability of these promising approaches by the research community . 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 . TD-Gammon uses a recently proposed reinforcement learning algorithm called TD(λ ) ( Sutton , 1988 ) , and is apparently the first application of this algorithm to a complex nontrivial task . Despite starting from random initial weights ( and hence random initial strategy ) , TD-Gammon achieves a surprisingly strong level of play . With zero knowledge built in at the start of learning ( i.e. given only a “ raw ” description of the board state ) , the network learns to play the entire game at a strong intermediate level that surpasses not only conventional commercial programs , but also comparable networks trained via supervised learning on a large corpus of human expert games . This paper shows that , for two popular algorithms , such oscillation is the worst that can happen : the weights can not diverge , but instead must converge to a bounded region . The algorithms are SARSA(0 ) and V(0 ) ; the latter algorithm was used in the well-known TD-Gammon program . TD-Gammon is a neural network that is able to teach itself to play backgammon solely by playing against itself and learning from the results . Starting from random initial play , TD-Gammon 's self-teaching methodology results in a surprisingly strong program : without lookahead , its positional judgement rivals that of human experts , and when combined with shallow lookahead , it reaches a level of play that surpasses even the best human players . The success of TD-Gammon has also been replicated by several other programmers ; at least two other neural net programs also appear to be capable of superhuman play . Previous papers on TD-Gammon have focused on developing a scientific understanding of its reinforcement learning methodology . This paper views machine learning as a tool in a programmer 's toolkit , and considers how it can be combined with other programming techniques to achieve and surpass world-class backgammon play . Particular emphasis is placed on programming shallow-depth search algorithms , and on TD-Gammon 's doubling algorithm , which is described in print here for the first time . Copyright 2002 Elsevier Science B.V. In recent years , neural networks have enjoyed a renaissance as function approximators in reinforcement learning . Two decades after Tesauro 's TD-Gammon achieved near top-level human performance in backgammon , the deep reinforcement learning algorithm DQN achieved human-level performance in many Atari 2600 games . The purpose of this study is twofold . This chapter starts with presentation of two notable examples of CI application to mind game playing : Tesauro ’s Neurogammon and TD-Gammon systems playing backgammon ( chapter 6.1 ) , and Chellapilla and Fogel ’s Blondie24 approach to checkers ( chapter 6.2 ) . Both these systems are milestone accomplishments in CI-based game playing and both had inspired several developments that followed their underlying concepts . However , in real-world problems the dynamics governing the environment are often complex and unknown . In this work we present the MuZero algorithm which , by combining a tree-based search with a learned model , achieves superhuman performance in a range of challenging and visually complex domains , without any knowledge of their underlying dynamics . MuZero learns a model that , when applied iteratively , predicts the quantities most directly relevant to planning : the reward , the action-selection policy , and the value function . When evaluated on 57 different Atari games - the canonical video game environment for testing AI techniques , in which model-based planning approaches have historically struggled - our new algorithm achieved a new state of the art . When evaluated on Go , chess and shogi , without any knowledge of the game rules , MuZero matched the superhuman performance of the AlphaZero algorithm that was supplied with the game rules .
The US Naval Postgraduate School and University of Washington each independently developed informal security-themed tabletop games . [ d0x3d ! ] is a board game in which players collaborate as white-hat hackers , tasked to retrieve a set of valuable digital assets held by an adversarial network . Control-Alt-Hack is a card game in which three to six players act as white-hat hackers at a security consulting company .
Language games are important to create excitement in language lessons . This session reports on a study that aimed to explore the benefits of using board game to teach grammar . The study was carried out among Pre TESL students of UiTM Pahang , Kuantan Campus .
Also this method requires a complex logic loop for finding idle periods . In this paper , we present another technique using game-board which is simple to implement and uses native data structures . However , this simplicity comes at a performance cost which has also been analyzed in this paper .
Said first information item may include plain text , pictures , frames , video , word-processor data , spread-sheet data , game figures , game elements , text or picture messages , and combinations thereof . Said step of determining the first location of the game element includes the steps of : transmitting , by at least one transmitter located on the game element , at least one signal identifying said game element ; receiving , by at least one sensor ( 41 ) located on the game board , at least one identifying signal ; and determining , by the game board , the first location based on at least one identifying signal . This allows addition of new game elements to a game without actually new elements being bought because modified information presented on said game elements may modify their kind and appearance .
The invention provides a board game board having a respectively different unique information for each geographic information is displayed and the position is the geographical information is displayed , and the game customized information , for example area information , tourist information , cultural assets information , restaurants information and the like corresponding to the unique information that can be displayed in the portable terminal of the player to participate in the present invention relates to " in the information of the board the board can be displayed on a mobile terminal board game apparatus and method " . Board game apparatus which can display information on the board , the board according to the invention the mobile terminal board in a built-in claim 1 RF tag to display the geographical information and a built-in respectively different unique information for each position at which the geographical information is displayed game as the second plate and , when the game is in progress player capable of mounting to move with the position of the board , the board tools , and wherein the 1 RF tag and a wireless linkage to leading the the unique information embedded in the claim 1 RF tag RF tag , the wireless transmission of the player tools , the player tool and the second control unit to receive sending the unique information reading from the 2 RF tag , received from the control unit transmits the unique information to a radio transmitter for transmitting over the air It includes a mobile terminal having a wireless receiving unit receiving the unique information that are sent from the player , The sphere is the radio transmitting apparatus and radio linked mobile terminal is characterized in that it displays the customized information corresponding to the unique information on the display unit .
The game market is an increasingly large industry . The board-game market , which is the most traditional in the game market , continues to show a steady growth . It is very important for both publishers and players to predict the propensity of users in this huge market and to recommend new games .
In many cases , cyber theft happened due to a lack of understanding of how to secure a single piece of private information . In the present paper , we attempt to provide an inexpensive and easy tool for learning cybersecurity through a board game simulation called Security Defense and Dungeons ( sD&D ) . The basic concept is not only to introduce the latest cybersecurity equipment and solutions , but also to provide cybersecurity education for players to raise awareness regarding cybersecurity threats .
Mo Gem game is a board game type game that uses patterned board as its playground . Mo Gem game is played by two players in play turns with five pieces of game tools in the form of pebbles , seeds of sapodilla and other grains owned by every player .
In this paper , a mobile competition game was developed in the form of a competitive board game for engaging students in learning local cultural . The students were asked to learn with the mobile game by throwing a dice to determine their moves on a tablet computer .
Besides the entertainment , this game can train and improve the power thought concentration and agility of PC users . Othello Game is a game boardgame using intelligence arti- cial ( Arti cial Intelligent ) . AI in this game are located on every level-level , among which the level will make random easily placement and may clause Othello placed anywhere , the level is going to nd the greatest value for obtain max- imum results , and level of diculty , will make predictions to the user and will search for the value at the corner of the game board .
Hex [ 1 ] is a two player board game which is traditionally played on a rhombic hexagonal pattern ( See Figure ( 1 ) ) . Players are assigned a colour and make moves by putting a token of their colour onto an empty field on the board .
AlphaZero has been very successful in many games . Unfortunately , it still consumes a huge amount of computing resources , the majority of which is spent in self-play .
This paper proposes using population based training ( PBT ) to help tune hyperparameters dynamically and improve strength during training time . Another significant advantage is that this method requires a single run only , while incurring a small additional time cost , since the time for generating self-play records remains unchanged though the time for optimization is increased following the AlphaZero training algorithm . In our experiments for 9x9 Go , the PBT method is able to achieve a higher win rate for 9x9 Go than the baselines , each with its own hyperparameter configuration and trained individually . For 19x19 Go , with PBT , we are able to obtain improvements in playing strength . Specifically , the PBT agent can obtain up to 74 % win rate against ELF OpenGo , an open-source state-of-the-art AlphaZero program using a neural network of a comparable capacity . This is compared to a saturated non-PBT agent , which achieves a win rate of 47 % against ELF OpenGo under the same circumstances .
AlphaZero learns to play go , chess and shogi at a superhuman level through self play given only the rules of the game . This raises the question of whether a similar thing could be done for mathematics -- a MathZero .
Most of these algorithms are actually more or less based on human knowledge or thinking ways to model network entities , environments , and rules , and the accuracy and rationality of models are not strong enough to match the changed battlefield . The AlphaZero algorithm generalised in chess and other games provides a new intelligent method to solve complex problems in the military field . Based on the AlphaZero algorithm , this study proposes a method for intelligent deployment of mobile ad-hoc networks with tactical communication node vehicles .
In this paper , we mainly focus on the Big-win strategy to improve the performance of AlphaZero without human knowledge . We are pleased to derive some promising results in which our Big-win approach has improved the strength of the 6x6 Othello program with win rate=63 % , lose rate=28 % , and draw rate=9 % comparing to the original AlphaZero approach based on a fair training and playing time conditions .
Therefore , we introduce the Two-Phase-Win strategy to combine AlphaZero and miniMax search with alpha-beta pruning for improving AlphaZero ’s strength . It has improved the strength of the AlphaZero approach applied to Connect4 . The results of experiments show that the Two-Phase-Win strategy has 58 % win rate against the AlphaZero approach and does n’t lose any game in a 100-game match .
AlphaZero Self-Play Search Enhancements . Recently , AlphaZero has achieved landmark results in deep reinforcement learning , by providing a single self-play architecture that learned three different games at super human level . AlphaZero is a large and complicated system with many parameters , and success requires much compute power and fine-tuning . Reproducing results in other games is a challenge , and many researchers are looking for ways to improve results while reducing computational demands . AlphaZero 's design is purely based on self-play and makes no use of labeled expert data ordomain specific enhancements ; it is designed to learn from scratch . We propose a novel approach to deal with this cold-start problem by employing simple search enhancements at the beginning phase of self-play training , namely Rollout , Rapid Action Value Estimate ( RAVE ) and dynamically weighted combinations of these with the neural network , and Rolling Horizon Evolutionary Algorithms ( RHEA ) .
The success of AlphaZero algorithm in chess games provides ideas and methods for other fields . This research designs an intelligent deployment model of wireless ad hoc network node based on the deep reinforcement learning framework of AlphaZero algorithm . Aiming at the difficult problems of setting learning rate and preventing model overfitting in the process of model self-training optimization , this paper chooses gradually decreasing dynamics .
The AlphaGo , AlphaGo Zero , and AlphaZero series of algorithms are remarkable demonstrations of deep reinforcement learning 's capabilities , achieving superhuman performance in the complex game of Go with progressively increasing autonomy . However , many obstacles remain in the understanding of and usability of these promising approaches by the research community .
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 .
TD-Gammon uses a recently proposed reinforcement learning algorithm called TD(λ ) ( Sutton , 1988 ) , and is apparently the first application of this algorithm to a complex nontrivial task . Despite starting from random initial weights ( and hence random initial strategy ) , TD-Gammon achieves a surprisingly strong level of play . With zero knowledge built in at the start of learning ( i.e. given only a “ raw ” description of the board state ) , the network learns to play the entire game at a strong intermediate level that surpasses not only conventional commercial programs , but also comparable networks trained via supervised learning on a large corpus of human expert games .
This paper shows that , for two popular algorithms , such oscillation is the worst that can happen : the weights can not diverge , but instead must converge to a bounded region . The algorithms are SARSA(0 ) and V(0 ) ; the latter algorithm was used in the well-known TD-Gammon program .
TD-Gammon is a neural network that is able to teach itself to play backgammon solely by playing against itself and learning from the results . Starting from random initial play , TD-Gammon 's self-teaching methodology results in a surprisingly strong program : without lookahead , its positional judgement rivals that of human experts , and when combined with shallow lookahead , it reaches a level of play that surpasses even the best human players . The success of TD-Gammon has also been replicated by several other programmers ; at least two other neural net programs also appear to be capable of superhuman play . Previous papers on TD-Gammon have focused on developing a scientific understanding of its reinforcement learning methodology . This paper views machine learning as a tool in a programmer 's toolkit , and considers how it can be combined with other programming techniques to achieve and surpass world-class backgammon play . Particular emphasis is placed on programming shallow-depth search algorithms , and on TD-Gammon 's doubling algorithm , which is described in print here for the first time . Copyright 2002 Elsevier Science B.V.
In recent years , neural networks have enjoyed a renaissance as function approximators in reinforcement learning . Two decades after Tesauro 's TD-Gammon achieved near top-level human performance in backgammon , the deep reinforcement learning algorithm DQN achieved human-level performance in many Atari 2600 games . The purpose of this study is twofold .
This chapter starts with presentation of two notable examples of CI application to mind game playing : Tesauro ’s Neurogammon and TD-Gammon systems playing backgammon ( chapter 6.1 ) , and Chellapilla and Fogel ’s Blondie24 approach to checkers ( chapter 6.2 ) . Both these systems are milestone accomplishments in CI-based game playing and both had inspired several developments that followed their underlying concepts .
However , in real-world problems the dynamics governing the environment are often complex and unknown . In this work we present the MuZero algorithm which , by combining a tree-based search with a learned model , achieves superhuman performance in a range of challenging and visually complex domains , without any knowledge of their underlying dynamics . MuZero learns a model that , when applied iteratively , predicts the quantities most directly relevant to planning : the reward , the action-selection policy , and the value function . When evaluated on 57 different Atari games - the canonical video game environment for testing AI techniques , in which model-based planning approaches have historically struggled - our new algorithm achieved a new state of the art . When evaluated on Go , chess and shogi , without any knowledge of the game rules , MuZero matched the superhuman performance of the AlphaZero algorithm that was supplied with the game rules .
Security through play Enhancing Grammar Using Board Game Determining Actual Response Time in P-FRP Using Idle-Period Game Board Game board and game element with detecting means Board game system with mobile terminal displaying information of board game plate Sequential Recommendations on Board-Game Platforms sD&D: Design and Implementation of Cybersecurity Educational Game with Highly Extensible Functionality Aplikasi Permainan Tradisional Mo Gem Menggunakan Algoritma Minimax dengan Optimasi Alpha Beta Pruning pada Platform pada Platform Android An Experiment of a Mobile Competition Game for Investigating Students' Interests in Learning Local Culture GAME PROGRAMS USING JAVA2SDK Othello HexGraph: Applying Graph Drawing Algorithms to the Game of Hex Accelerating and Improving AlphaZero Using Population Based Training MathZero, The Classification Problem, and Set-Theoretic Type Theory Deploying tactical communication node vehicles with AlphaZero algorithm The Big Win Strategy on Multi-Value Network: An Improvement over AlphaZero Approach for 6x6 Othello Two-Phase-Win Strategy for Improving the AlphaZero’s Strength Warm-Start AlphaZero Self-Play Search Enhancements Optimization of Wireless Ad Hoc Network Node Layout Self-play Based on AlphaZero Algorithm ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm TD-Gammon: A Self-Teaching Backgammon Program Reinforcement Learning with Function Approximation Converges to a Region Programming backgammon using self-teaching neural nets Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning CI in Games – Selected Approaches Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
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ISORC ICALT Graph Drawing AAAI ArXiv NIPS
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