Pub Date : 2015-03-05DOI: 10.1109/TCIAIG.2015.2401979
Emiliano G. Castro, M. Tsuzuki
The rise of free-to-play and other service-based business models in the online gaming market brought to game publishers problems usually associated to markets like mobile telecommunications and credit cards, especially customer churn. Predictive models have long been used to address this issue in these markets, where companies have a considerable amount of demographic, economic, and behavioral data about their customers, while online game publishers often only have behavioral data. Simple time series' feature representation schemes like RFM can provide reasonable predictive models solely based on online game players' login records, but maybe without fully exploring the predictive potential of these data. We propose a frequency analysis approach for feature representation from login records for churn prediction modeling. These entries (from real data) were converted into fixed-length data arrays using four different methods, and then these were used as input for training probabilistic classifiers with the k-nearest neighbors machine learning algorithm. The classifiers were then evaluated and compared using predictive performance metrics. One of the methods, the time-frequency plane domain analysis, showed satisfactory results, being able to theoretically increase the retention campaigns profits in more than 20% over the RFM approach.
{"title":"Churn Prediction in Online Games Using Players’ Login Records: A Frequency Analysis Approach","authors":"Emiliano G. Castro, M. Tsuzuki","doi":"10.1109/TCIAIG.2015.2401979","DOIUrl":"https://doi.org/10.1109/TCIAIG.2015.2401979","url":null,"abstract":"The rise of free-to-play and other service-based business models in the online gaming market brought to game publishers problems usually associated to markets like mobile telecommunications and credit cards, especially customer churn. Predictive models have long been used to address this issue in these markets, where companies have a considerable amount of demographic, economic, and behavioral data about their customers, while online game publishers often only have behavioral data. Simple time series' feature representation schemes like RFM can provide reasonable predictive models solely based on online game players' login records, but maybe without fully exploring the predictive potential of these data. We propose a frequency analysis approach for feature representation from login records for churn prediction modeling. These entries (from real data) were converted into fixed-length data arrays using four different methods, and then these were used as input for training probabilistic classifiers with the k-nearest neighbors machine learning algorithm. The classifiers were then evaluated and compared using predictive performance metrics. One of the methods, the time-frequency plane domain analysis, showed satisfactory results, being able to theoretically increase the retention campaigns profits in more than 20% over the RFM approach.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"7 1","pages":"255-265"},"PeriodicalIF":0.0,"publicationDate":"2015-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2015.2401979","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62592626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An approach called generic job-level (JL) search was proposed to solve computer game applications by dispatching jobs to remote workers for parallel processing. This paper applies JL search to alpha-beta search, and proposes a JL alpha-beta search (JL-ABS) algorithm based on a best-first search version of MTD(f). The JL-ABS algorithm is demonstrated by using it in an opening book analysis for Chinese chess. The experimental results demonstrated that JL-ABS reached a speed-up of 10.69 when using 16 workers in the JL system.
{"title":"Job-Level Alpha-Beta Search","authors":"Jr-Chang Chen, I-Chen Wu, Wen-Jie Tseng, Bo-Han Lin, Chia-Hui Chang","doi":"10.1109/TCIAIG.2014.2316314","DOIUrl":"https://doi.org/10.1109/TCIAIG.2014.2316314","url":null,"abstract":"An approach called generic job-level (JL) search was proposed to solve computer game applications by dispatching jobs to remote workers for parallel processing. This paper applies JL search to alpha-beta search, and proposes a JL alpha-beta search (JL-ABS) algorithm based on a best-first search version of MTD(f). The JL-ABS algorithm is demonstrated by using it in an opening book analysis for Chinese chess. The experimental results demonstrated that JL-ABS reached a speed-up of 10.69 when using 16 workers in the JL system.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"7 1","pages":"28-38"},"PeriodicalIF":0.0,"publicationDate":"2015-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2014.2316314","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62591901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-03-01DOI: 10.1109/TCIAIG.2014.2329034
Shi-Jim Yen, Cheng-Wei Chou, Jr-Chang Chen, I-Chen Wu, Kuo-Yuan Kao
Chinese Dark Chess is an old and very popular game in the Chinese culture sphere. This game is a stochastic game with symmetric hidden information. This paper reviews alpha-beta search with chance nodes and proposes heuristics on Chinese Dark Chess programs. We propose an application of nondeterministic Monte Carlo Tree Search with random nodes for tackling partial observation. The proposed methods were implemented in the program Diablo, which won four Chinese Dark Chess tournaments in TAAI 2011/2012, TCGA 2011/2012 computer game tournaments. Diablo also played hundreds of games with different human players and programs based on alpha-beta search. These results show that the nondeterministic MCTS equipped with our heuristics is promising for Chinese Dark Chess.
{"title":"Design and Implementation of Chinese Dark Chess Programs","authors":"Shi-Jim Yen, Cheng-Wei Chou, Jr-Chang Chen, I-Chen Wu, Kuo-Yuan Kao","doi":"10.1109/TCIAIG.2014.2329034","DOIUrl":"https://doi.org/10.1109/TCIAIG.2014.2329034","url":null,"abstract":"Chinese Dark Chess is an old and very popular game in the Chinese culture sphere. This game is a stochastic game with symmetric hidden information. This paper reviews alpha-beta search with chance nodes and proposes heuristics on Chinese Dark Chess programs. We propose an application of nondeterministic Monte Carlo Tree Search with random nodes for tackling partial observation. The proposed methods were implemented in the program Diablo, which won four Chinese Dark Chess tournaments in TAAI 2011/2012, TCGA 2011/2012 computer game tournaments. Diablo also played hundreds of games with different human players and programs based on alpha-beta search. These results show that the nondeterministic MCTS equipped with our heuristics is promising for Chinese Dark Chess.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"7 1","pages":"66-74"},"PeriodicalIF":0.0,"publicationDate":"2015-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2014.2329034","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62592115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-03-01DOI: 10.1109/TCIAIG.2014.2329770
M. Ersen, Sanem Sariel
In this paper, we introduce an automated reasoning system for learning object behaviors and interactions through the observation of event sequences. We use an existing system to learn the models of objects and further extend it to model more complex behaviors. Furthermore, we propose a spatio-temporal reasoning based learning method for reasoning about interactions among objects. Experience gained through learning is to be used for achieving goals by these objects. We take The Incredible Machine game (TIM) as the main testbed to analyze our system. Tutorials of the game are used to train the system. We analyze the results of our reasoning system on four different input types: a knowledge base of relations; spatial information; temporal information; and spatio-temporal information from the environment. Our analysis reveals that if a knowledge base about relations is provided, most of the interactions can be learned. We have also demonstrated that our learning method which incorporates both spatial and temporal information gives close results to that of the knowledge-based approach. This is promising as gathering spatio-temporal information does not require prior knowledge about relations. Our second analysis of the spatio-temporal reasoning method in the Electric Box computer game domain verifies the success of our approach.
{"title":"Learning Behaviors of and Interactions Among Objects Through Spatio–Temporal Reasoning","authors":"M. Ersen, Sanem Sariel","doi":"10.1109/TCIAIG.2014.2329770","DOIUrl":"https://doi.org/10.1109/TCIAIG.2014.2329770","url":null,"abstract":"In this paper, we introduce an automated reasoning system for learning object behaviors and interactions through the observation of event sequences. We use an existing system to learn the models of objects and further extend it to model more complex behaviors. Furthermore, we propose a spatio-temporal reasoning based learning method for reasoning about interactions among objects. Experience gained through learning is to be used for achieving goals by these objects. We take The Incredible Machine game (TIM) as the main testbed to analyze our system. Tutorials of the game are used to train the system. We analyze the results of our reasoning system on four different input types: a knowledge base of relations; spatial information; temporal information; and spatio-temporal information from the environment. Our analysis reveals that if a knowledge base about relations is provided, most of the interactions can be learned. We have also demonstrated that our learning method which incorporates both spatial and temporal information gives close results to that of the knowledge-based approach. This is promising as gathering spatio-temporal information does not require prior knowledge about relations. Our second analysis of the spatio-temporal reasoning method in the Electric Box computer game domain verifies the success of our approach.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"7 1","pages":"75-87"},"PeriodicalIF":0.0,"publicationDate":"2015-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2014.2329770","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62592173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-03-01DOI: 10.1109/TCIAIG.2014.2309077
Jiaxing Song, Martin Müller
The game of Amazons is a modern board game with simple rules and nice mathematical properties. It has a high computational complexity. In 2001, the starting position on a 5 × 5 board was proven to be a first player win. The enhanced Amazons solver presented here extends previous work in the following five ways: by building more powerful endgame databases, including a new type of databases for so-called blocker territories, by improving the rules for computing bounds on complex game positions, by local search to find tighter local bounds, by using ideas from combinatorial game theory to find wins earlier, and by using a df-pn based solver. Using the improved solver, the starting positions for Amazons on the 4 × 5, 5 × 4, 4 × 6, 5 × 6, and 4 × 7 boards were shown to be first player wins, while 6 × 4 is a second player win. The largest proof, for the 5 × 6 board, is presented in detail.
{"title":"An Enhanced Solver for the Game of Amazons","authors":"Jiaxing Song, Martin Müller","doi":"10.1109/TCIAIG.2014.2309077","DOIUrl":"https://doi.org/10.1109/TCIAIG.2014.2309077","url":null,"abstract":"The game of Amazons is a modern board game with simple rules and nice mathematical properties. It has a high computational complexity. In 2001, the starting position on a 5 × 5 board was proven to be a first player win. The enhanced Amazons solver presented here extends previous work in the following five ways: by building more powerful endgame databases, including a new type of databases for so-called blocker territories, by improving the rules for computing bounds on complex game positions, by local search to find tighter local bounds, by using ideas from combinatorial game theory to find wins earlier, and by using a df-pn based solver. Using the improved solver, the starting positions for Amazons on the 4 × 5, 5 × 4, 4 × 6, 5 × 6, and 4 × 7 boards were shown to be first player wins, while 6 × 4 is a second player win. The largest proof, for the 5 × 6 board, is presented in detail.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"7 1","pages":"16-27"},"PeriodicalIF":0.0,"publicationDate":"2015-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2014.2309077","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62591767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-03-01DOI: 10.1109/TCIAIG.2014.2317737
T. Cazenave
Monte Carlo tree search (MCTS) is state of the art for multiple games and problems. The base algorithm currently used for MCTS is UCT. We propose an alternative MCTS algorithm: sequential halving applied to Trees (SHOT). It has multiple advantages over UCT: it spends less time in the tree, it uses less memory, it is parameter free, at equal time settings it beats UCT for a complex combinatorial game and it can be efficiently parallelized.
{"title":"Sequential Halving Applied to Trees","authors":"T. Cazenave","doi":"10.1109/TCIAIG.2014.2317737","DOIUrl":"https://doi.org/10.1109/TCIAIG.2014.2317737","url":null,"abstract":"Monte Carlo tree search (MCTS) is state of the art for multiple games and problems. The base algorithm currently used for MCTS is UCT. We propose an alternative MCTS algorithm: sequential halving applied to Trees (SHOT). It has multiple advantages over UCT: it spends less time in the tree, it uses less memory, it is parameter free, at equal time settings it beats UCT for a complex combinatorial game and it can be efficiently parallelized.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"7 1","pages":"102-105"},"PeriodicalIF":0.0,"publicationDate":"2015-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2014.2317737","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62591967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-03-01DOI: 10.1109/TCIAIG.2014.2323894
Yun-Gyung Cheong, R. Young
Interactive storytelling has been receiving a growing attention from AI and game communities and a number of computational approaches have shown promises in generating stories for games. However, there has been little research on stories evoking specific cognitive and affective responses. The goal of the work we describe here is to develop a system that produces a narrative designed specifically to arouse suspense from its reader. Our approach attempts to create stories that manipulate the reader's suspense level by elaborating on the story structure that can influence the reader's narrative comprehension at a specific point in her reading. Adapting theories developed by cognitive psychologists, our approach uses a plan-based model of narrative comprehension to determine the final content of the story in order to manipulate the reader's suspense. In this paper, we describe our system implementation and empirical evaluations to test the efficacy of this system.
{"title":"Suspenser: A Story Generation System for Suspense","authors":"Yun-Gyung Cheong, R. Young","doi":"10.1109/TCIAIG.2014.2323894","DOIUrl":"https://doi.org/10.1109/TCIAIG.2014.2323894","url":null,"abstract":"Interactive storytelling has been receiving a growing attention from AI and game communities and a number of computational approaches have shown promises in generating stories for games. However, there has been little research on stories evoking specific cognitive and affective responses. The goal of the work we describe here is to develop a system that produces a narrative designed specifically to arouse suspense from its reader. Our approach attempts to create stories that manipulate the reader's suspense level by elaborating on the story structure that can influence the reader's narrative comprehension at a specific point in her reading. Adapting theories developed by cognitive psychologists, our approach uses a plan-based model of narrative comprehension to determine the final content of the story in order to manipulate the reader's suspense. In this paper, we describe our system implementation and empirical evaluations to test the efficacy of this system.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"7 1","pages":"39-52"},"PeriodicalIF":0.0,"publicationDate":"2015-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2014.2323894","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62591979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-03-01DOI: 10.1109/TCIAIG.2014.2326012
D. Ashlock, J. A. Brown, P. Hingston
Agents for playing iterated prisoner's dilemma are commonly trained using a coevolutionary system in which a player's score against a selection of other members of an evolving population forms the fitness function. In this study we examine instead a version of evolutionary iterated prisoner's dilemma in which an agent's fitness is measured as the average score it obtains against a fixed panel of opponents called an examination board. The performance of agents trained using examination boards is compared against agents trained in the usual coevolutionary fashion. This includes assessing the relative competitive ability of players evolved with evolution and coevolution. The difficulty of several experimental boards as optimization problems is compared. A number of new types of strategies are introduced. These include sugar strategies which can be exploited with some difficulty and treasure hunt strategies which have multiple trapping states with different levels of exploitability. The degree to which strategies trained with different examination boards produce different agents is investigated using fingerprints.
{"title":"Multiple Opponent Optimization of Prisoner’s Dilemma Playing Agents","authors":"D. Ashlock, J. A. Brown, P. Hingston","doi":"10.1109/TCIAIG.2014.2326012","DOIUrl":"https://doi.org/10.1109/TCIAIG.2014.2326012","url":null,"abstract":"Agents for playing iterated prisoner's dilemma are commonly trained using a coevolutionary system in which a player's score against a selection of other members of an evolving population forms the fitness function. In this study we examine instead a version of evolutionary iterated prisoner's dilemma in which an agent's fitness is measured as the average score it obtains against a fixed panel of opponents called an examination board. The performance of agents trained using examination boards is compared against agents trained in the usual coevolutionary fashion. This includes assessing the relative competitive ability of players evolved with evolution and coevolution. The difficulty of several experimental boards as optimization problems is compared. A number of new types of strategies are introduced. These include sugar strategies which can be exploited with some difficulty and treasure hunt strategies which have multiple trapping states with different levels of exploitability. The degree to which strategies trained with different examination boards produce different agents is investigated using fingerprints.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"7 1","pages":"53-65"},"PeriodicalIF":0.0,"publicationDate":"2015-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2014.2326012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62592052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-01-20DOI: 10.1109/TCIAIG.2015.2393433
S. Tekofsky, P. Spronck, M. Goudbeek, A. Plaat, Jaap van den Herik
In recent decades, video games have come to appeal to people of all ages. The effect of age on how people play games is not fully understood. In this paper, we delve into the question how age relates to an individual's play style. “Play style” is defined as any (set of) patterns in game actions performed by a player. Based on data from 10 416 Battlefield 3 players, we found that age strongly correlates to how people start out playing a game (initial play style), and to how they change their play style over time (play style development). Our data shows three major trends: 1) correlations between age and initial play style peak around the age of 20; 2) performance decreases with age; and 3) speed of play decreases with age. The relationship between age and play style may be explained by the neurocognitive effects of aging: as people grow older, their cognitive performance decays, their personalities shift to a more conscientious style, and their gaming motivations become less achievement-oriented.
{"title":"Past Our Prime: A Study of Age and Play Style Development in Battlefield 3","authors":"S. Tekofsky, P. Spronck, M. Goudbeek, A. Plaat, Jaap van den Herik","doi":"10.1109/TCIAIG.2015.2393433","DOIUrl":"https://doi.org/10.1109/TCIAIG.2015.2393433","url":null,"abstract":"In recent decades, video games have come to appeal to people of all ages. The effect of age on how people play games is not fully understood. In this paper, we delve into the question how age relates to an individual's play style. “Play style” is defined as any (set of) patterns in game actions performed by a player. Based on data from 10 416 Battlefield 3 players, we found that age strongly correlates to how people start out playing a game (initial play style), and to how they change their play style over time (play style development). Our data shows three major trends: 1) correlations between age and initial play style peak around the age of 20; 2) performance decreases with age; and 3) speed of play decreases with age. The relationship between age and play style may be explained by the neurocognitive effects of aging: as people grow older, their cognitive performance decays, their personalities shift to a more conscientious style, and their gaming motivations become less achievement-oriented.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"7 1","pages":"292-303"},"PeriodicalIF":0.0,"publicationDate":"2015-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2015.2393433","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62592559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-12-01DOI: 10.1109/TCIAIG.2013.2275163
M. Świechowski, J. Mańdziuk
The term general game playing (GGP) refers to a subfield of AI which aims at developing agents able to effectively play many games from a particular class (finite, deterministic). It is also the name of the annual competition proposed by Stanford Logic Group at Stanford University (Stanford, CA, USA), which provides a framework for testing and evaluating GGP agents. In this paper, we present our GGP player which managed to win four out of seven games in the 2012 preliminary round and advanced to the final phase. Our system (named MINI-Player) relies on a pool of playing strategies and autonomously picks the ones which seem to be best suited to a given game. The chosen strategies are combined with one another and incorporated into the upper confidence bounds applied to trees (UCT) algorithm. The effectiveness of our player is evaluated on a set of games from the 2012 GGP Competition as well as a few other, single-player games. The paper discusses the efficacy of proposed playing strategies and evaluates the mechanism of their switching. The proposed idea of dynamically assigning search strategies during play is both novel and promising.
通用游戏玩法(GGP)指的是AI的一个子领域，它旨在开发能够有效地玩特定类别(有限的，确定性的)许多游戏的代理。这也是斯坦福大学(Stanford, CA, USA)斯坦福逻辑小组提出的年度竞赛的名称，该竞赛为测试和评估GGP代理提供了一个框架。在本文中，我们介绍了我们的GGP选手在2012年的预赛中取得了七场比赛中的四场胜利，并进入了决赛阶段。我们的系统(名为MINI-Player)依赖于一系列游戏策略，并自主选择最适合特定游戏的策略。将选择的策略相互组合，并纳入应用于树的上置信区间(UCT)算法。我们的玩家的有效性是根据2012年GGP竞赛的一系列游戏以及其他一些单人游戏来评估的。本文讨论了所提出的博弈策略的有效性，并评估了它们的转换机制。在游戏过程中动态分配搜索策略的想法既新颖又有前途。
{"title":"Self-Adaptation of Playing Strategies in General Game Playing","authors":"M. Świechowski, J. Mańdziuk","doi":"10.1109/TCIAIG.2013.2275163","DOIUrl":"https://doi.org/10.1109/TCIAIG.2013.2275163","url":null,"abstract":"The term general game playing (GGP) refers to a subfield of AI which aims at developing agents able to effectively play many games from a particular class (finite, deterministic). It is also the name of the annual competition proposed by Stanford Logic Group at Stanford University (Stanford, CA, USA), which provides a framework for testing and evaluating GGP agents. In this paper, we present our GGP player which managed to win four out of seven games in the 2012 preliminary round and advanced to the final phase. Our system (named MINI-Player) relies on a pool of playing strategies and autonomously picks the ones which seem to be best suited to a given game. The chosen strategies are combined with one another and incorporated into the upper confidence bounds applied to trees (UCT) algorithm. The effectiveness of our player is evaluated on a set of games from the 2012 GGP Competition as well as a few other, single-player games. The paper discusses the efficacy of proposed playing strategies and evaluates the mechanism of their switching. The proposed idea of dynamically assigning search strategies during play is both novel and promising.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"6 1","pages":"367-381"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2013.2275163","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62591104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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