Pub Date : 2016-09-01DOI: 10.1109/TCIAIG.2015.2439061
M. Gaudesi, Elio Piccolo, Giovanni Squillero, A. Tonda
The iterated prisoner's dilemma is a famous model of cooperation and conflict in game theory. Its origin can be traced back to the Cold War, and countless strategies for playing it have been proposed so far, either designed by hand or automatically generated by computers. In the 2000s, scholars started focusing on adaptive players, that is, able to classify their opponent's behavior and adopt an effective counter-strategy. The player presented in this paper, pushes such idea even further: it builds a model of the current adversary from scratch, without relying on any pre-defined archetypes, and tweaks it as the game develops using an evolutionary algorithm; at the same time, it exploits the model to lead the game into the most favorable continuation. Models are compact nondeterministic finite state machines; they are extremely efficient in predicting opponents' replies, without being completely correct by necessity. Experimental results show that such a player is able to win several one-to-one games against strong opponents taken from the literature, and that it consistently prevails in round-robin tournaments of different sizes.
{"title":"Exploiting Evolutionary Modeling to Prevail in Iterated Prisoner’s Dilemma Tournaments","authors":"M. Gaudesi, Elio Piccolo, Giovanni Squillero, A. Tonda","doi":"10.1109/TCIAIG.2015.2439061","DOIUrl":"https://doi.org/10.1109/TCIAIG.2015.2439061","url":null,"abstract":"The iterated prisoner's dilemma is a famous model of cooperation and conflict in game theory. Its origin can be traced back to the Cold War, and countless strategies for playing it have been proposed so far, either designed by hand or automatically generated by computers. In the 2000s, scholars started focusing on adaptive players, that is, able to classify their opponent's behavior and adopt an effective counter-strategy. The player presented in this paper, pushes such idea even further: it builds a model of the current adversary from scratch, without relying on any pre-defined archetypes, and tweaks it as the game develops using an evolutionary algorithm; at the same time, it exploits the model to lead the game into the most favorable continuation. Models are compact nondeterministic finite state machines; they are extremely efficient in predicting opponents' replies, without being completely correct by necessity. Experimental results show that such a player is able to win several one-to-one games against strong opponents taken from the literature, and that it consistently prevails in round-robin tournaments of different sizes.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2015.2439061","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62593065","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 : 2016-09-01DOI: 10.1109/TCIAIG.2015.2416206
S. Risi, J. Lehman, David B. D'Ambrosio, Ryan Hall, Kenneth O. Stanley
The impact of game content on the player experience is potentially more critical in casual games than in competitive games because of the diminished role of strategic or tactical diversions. Interestingly, until now procedural content generation (PCG) has nevertheless been investigated almost exclusively in the context of competitive, skills-based gaming. This paper therefore opens a new direction for PCG by placing it at the center of an entirely casual flower-breeding game platform called Petalz. That way, the behavior of players and their reactions to different game mechanics in a casual environment driven by PCG can be investigated. In particular, players in Petalz can: 1) trade their discoveries in a global marketplace; 2) respond to an incentive system that awards diversity; and 3) generate real-world 3-D replicas of their evolved flowers. With over 1900 registered online users and 38 646 unique evolved flowers, Petalz showcases the potential for PCG to enable these kinds of casual game mechanics, thus paving the way for continued innovation with PCG in casual gaming.
{"title":"Petalz: Search-Based Procedural Content Generation for the Casual Gamer","authors":"S. Risi, J. Lehman, David B. D'Ambrosio, Ryan Hall, Kenneth O. Stanley","doi":"10.1109/TCIAIG.2015.2416206","DOIUrl":"https://doi.org/10.1109/TCIAIG.2015.2416206","url":null,"abstract":"The impact of game content on the player experience is potentially more critical in casual games than in competitive games because of the diminished role of strategic or tactical diversions. Interestingly, until now procedural content generation (PCG) has nevertheless been investigated almost exclusively in the context of competitive, skills-based gaming. This paper therefore opens a new direction for PCG by placing it at the center of an entirely casual flower-breeding game platform called Petalz. That way, the behavior of players and their reactions to different game mechanics in a casual environment driven by PCG can be investigated. In particular, players in Petalz can: 1) trade their discoveries in a global marketplace; 2) respond to an incentive system that awards diversity; and 3) generate real-world 3-D replicas of their evolved flowers. With over 1900 registered online users and 38 646 unique evolved flowers, Petalz showcases the potential for PCG to enable these kinds of casual game mechanics, thus paving the way for continued innovation with PCG in casual gaming.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2015.2416206","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62592500","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 : 2016-09-01DOI: 10.1109/TCIAIG.2015.2443123
Hendrik Baier, M. Winands
Monte Carlo Tree Search (MCTS) is a popular approach for tree search in a variety of games. While MCTS allows for fine-grained time control, not much has been published on time management for MCTS programs under tournament conditions. This paper first investigates the effects of various time-management strategies on playing strength in the challenging game of Go. A number of domain-independent strategies are then tested in the domains Connect-4, Breakthrough, Othello, and Catch the Lion. We consider strategies taken from the literature as well as newly proposed and improved ones. Strategies include both semi-dynamic strategies that decide about time allocation for each search before it is started, and dynamic strategies that influence the duration of each move search while it is already running. Furthermore, we analyze the effects of time management strategies on the distribution of time over the moves of an average game, allowing us to partly explain their performance. In the experiments, the domain-independent strategy STOP provides a significant improvement over the state of the art in Go, and is the most effective time management strategy tested in all five domains.
蒙特卡罗树搜索(MCTS)是一种在各种游戏中流行的树搜索方法。虽然MCTS允许细粒度的时间控制，但关于比赛条件下MCTS程序的时间管理的文章还不多。本文首先研究了不同时间管理策略对围棋棋力的影响。然后，在Connect-4、Breakthrough、Othello和Catch the Lion领域中测试了许多与领域无关的策略。我们考虑从文献中采取的策略以及新提出的和改进的策略。策略既包括半动态策略(在每次搜索开始前决定其时间分配)，也包括动态策略(在每次移动搜索已经运行时影响其持续时间)。此外，我们分析了时间管理策略对平均游戏中移动时间分配的影响，使我们能够部分解释它们的表现。在实验中，与领域无关的STOP策略提供了对围棋最新状态的显著改进，并且是在所有五个领域中测试过的最有效的时间管理策略。
{"title":"Time Management for Monte Carlo Tree Search","authors":"Hendrik Baier, M. Winands","doi":"10.1109/TCIAIG.2015.2443123","DOIUrl":"https://doi.org/10.1109/TCIAIG.2015.2443123","url":null,"abstract":"Monte Carlo Tree Search (MCTS) is a popular approach for tree search in a variety of games. While MCTS allows for fine-grained time control, not much has been published on time management for MCTS programs under tournament conditions. This paper first investigates the effects of various time-management strategies on playing strength in the challenging game of Go. A number of domain-independent strategies are then tested in the domains Connect-4, Breakthrough, Othello, and Catch the Lion. We consider strategies taken from the literature as well as newly proposed and improved ones. Strategies include both semi-dynamic strategies that decide about time allocation for each search before it is started, and dynamic strategies that influence the duration of each move search while it is already running. Furthermore, we analyze the effects of time management strategies on the distribution of time over the moves of an average game, allowing us to partly explain their performance. In the experiments, the domain-independent strategy STOP provides a significant improvement over the state of the art in Go, and is the most effective time management strategy tested in all five domains.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2015.2443123","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62593125","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 : 2016-09-01DOI: 10.1109/TCIAIG.2015.2391232
M. Świechowski, J. Mańdziuk, Y. Ong
General game playing (GGP) aims at designing autonomous agents capable of playing any game within a certain genre, without human intervention. GGP agents accept the rules, which are written in the logic-based game definition language (GDL) and unknown to them beforehand, at runtime. The state-of-the-art players use Monte Carlo tree search (MCTS) together with the upper confidence bounds applied to trees (UCT) method. In this paper, we discuss several enhancements to GGP players geared towards more effective playing of single-player games within the MCTS/UCT framework. The main proposed improvements include introduction of a collection of lightweight policies which can be used for guiding the MCTS and a GGP-friendly way of using transposition tables. We have tested our base player and a specialized version of it for single-player games in a series of experiments using ten single-player games of various complexity. It is clear from the results that the optimized version of the player achieves significantly better performance. Furthermore, in the same set of tests against publicly available version of CadiaPlayer, one of the strongest GGP agents, the results are also favorable to the enhanced version of our player.
通用游戏(General game playing, GGP)旨在设计能够在没有人为干预的情况下玩特定类型的任何游戏的自主代理。GGP代理在运行时接受用基于逻辑的游戏定义语言(GDL)编写的规则，并且事先不知道这些规则。最先进的球员使用蒙特卡洛树搜索(MCTS)和上置信限应用于树(UCT)方法。在本文中，我们讨论了GGP玩家在MCTS/UCT框架内更有效地玩单人游戏的几个增强功能。提出的主要改进包括引入一组可用于指导MCTS的轻量级策略，以及使用换位表的ggp友好方式。我们使用10款不同复杂度的单人游戏进行了一系列实验，测试了我们的基本玩家和专门的单人游戏版本。从结果中我们可以清楚地看到，优化后的玩家能够获得更好的表现。此外，在同一组测试中，针对公开版本的CadiaPlayer，最强的GGP代理之一，结果也有利于我们的播放器的增强版本。
{"title":"Specialization of a UCT-Based General Game Playing Program to Single-Player Games","authors":"M. Świechowski, J. Mańdziuk, Y. Ong","doi":"10.1109/TCIAIG.2015.2391232","DOIUrl":"https://doi.org/10.1109/TCIAIG.2015.2391232","url":null,"abstract":"General game playing (GGP) aims at designing autonomous agents capable of playing any game within a certain genre, without human intervention. GGP agents accept the rules, which are written in the logic-based game definition language (GDL) and unknown to them beforehand, at runtime. The state-of-the-art players use Monte Carlo tree search (MCTS) together with the upper confidence bounds applied to trees (UCT) method. In this paper, we discuss several enhancements to GGP players geared towards more effective playing of single-player games within the MCTS/UCT framework. The main proposed improvements include introduction of a collection of lightweight policies which can be used for guiding the MCTS and a GGP-friendly way of using transposition tables. We have tested our base player and a specialized version of it for single-player games in a series of experiments using ten single-player games of various complexity. It is clear from the results that the optimized version of the player achieves significantly better performance. Furthermore, in the same set of tests against publicly available version of CadiaPlayer, one of the strongest GGP agents, the results are also favorable to the enhanced version of our player.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2015.2391232","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62592542","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 : 2016-09-01DOI: 10.1109/TCIAIG.2014.2377250
M. Dror, G. Kendall, A. Rapoport
Goofspiel is a simple two-person zero-sum game for which there exist no known equilibrium strategies. To gain insight into what constitute winning strategies, we conducted a round-robin tournament in which participants were asked to provide computerized programs for playing the game with or without carryover. Each of these two variants was to be played under two quite different objective functions, namely, maximization of the cumulative number of points won across all opponents (as in Axelrod's tournament), and maximization of the probability of winning any given round. Our results show that there are, indeed, inherent differences in the results with respect to the complexity of the game and its objective function, and that winning strategies exhibit a level of sophistication, depth, and balance that are not captured by present models of adaptive learning.
{"title":"Elicitation of Strategies in Four Variants of a Round-Robin Tournament: The Case of Goofspiel","authors":"M. Dror, G. Kendall, A. Rapoport","doi":"10.1109/TCIAIG.2014.2377250","DOIUrl":"https://doi.org/10.1109/TCIAIG.2014.2377250","url":null,"abstract":"Goofspiel is a simple two-person zero-sum game for which there exist no known equilibrium strategies. To gain insight into what constitute winning strategies, we conducted a round-robin tournament in which participants were asked to provide computerized programs for playing the game with or without carryover. Each of these two variants was to be played under two quite different objective functions, namely, maximization of the cumulative number of points won across all opponents (as in Axelrod's tournament), and maximization of the probability of winning any given round. Our results show that there are, indeed, inherent differences in the results with respect to the complexity of the game and its objective function, and that winning strategies exhibit a level of sophistication, depth, and balance that are not captured by present models of adaptive learning.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2014.2377250","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62592933","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}
Szubert and Jaśkowski successfully used temporal difference (TD) learning together with n -tuple networks for playing the game 2048. However, we observed a phenomenon that the programs based on TD learning still hardly reach large tiles. In this paper, we propose multistage TD (MS-TD) learning, a kind of hierarchical reinforcement learning method, to effectively improve the performance for the rates of reaching large tiles, which are good metrics to analyze the strength of 2048 programs. Our experiments showed significant improvements over the one without using MS-TD learning. Namely, using 3-ply expectimax search, the program with MS-TD learning reached 32768-tiles with a rate of 18.31%, while the one with TD learning did not reach any. After further tuned, our 2048 program reached 32768-tiles with a rate of 31.75% in 10,000 games, and one among these games even reached a 65536-tiles, which is the first ever reaching a 65536-tiles to our knowledge. In addition, MS-TD learning method can be easily applied to other 2048-like games, such as Threes. Based on MS-TD learning, our experiments for Threes also demonstrated similar performance improvement, where the program with MS-TD learning reached 6144-tiles with a rate of 7.83%, while the one with TD learning only reached 0.45%.
{"title":"Multistage Temporal Difference Learning for 2048-Like Games","authors":"Kun-Hao Yeh, I-Chen Wu, Chu-Hsuan Hsueh, Chia-Chuan Chang, Chao-Chin Liang, Chiang Han","doi":"10.1109/TCIAIG.2016.2593710","DOIUrl":"https://doi.org/10.1109/TCIAIG.2016.2593710","url":null,"abstract":"Szubert and Jaśkowski successfully used temporal difference (TD) learning together with n -tuple networks for playing the game 2048. However, we observed a phenomenon that the programs based on TD learning still hardly reach large tiles. In this paper, we propose multistage TD (MS-TD) learning, a kind of hierarchical reinforcement learning method, to effectively improve the performance for the rates of reaching large tiles, which are good metrics to analyze the strength of 2048 programs. Our experiments showed significant improvements over the one without using MS-TD learning. Namely, using 3-ply expectimax search, the program with MS-TD learning reached 32768-tiles with a rate of 18.31%, while the one with TD learning did not reach any. After further tuned, our 2048 program reached 32768-tiles with a rate of 31.75% in 10,000 games, and one among these games even reached a 65536-tiles, which is the first ever reaching a 65536-tiles to our knowledge. In addition, MS-TD learning method can be easily applied to other 2048-like games, such as Threes. Based on MS-TD learning, our experiments for Threes also demonstrated similar performance improvement, where the program with MS-TD learning reached 6144-tiles with a rate of 7.83%, while the one with TD learning only reached 0.45%.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2016.2593710","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62593103","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 : 2016-06-01DOI: 10.1109/TCIAIG.2015.2509600
Francesco Calimeri, Michael Fink, Stefano Germano, Giovambattista Ianni, Christoph Redl, Anton Wimmer
This paper presents the Angry-HEX artificial intelligent agent that participated in the 2013 and 2014 Angry Birds Artificial Intelligence Competitions. The agent has been developed in the context of a joint project between the University of Calabria (UniCal) and the Vienna University of Technology (TU Vienna). The specific issues that arise when introducing artificial intelligence in a physics-based game are dealt with a combination of traditional imperative programming and declarative programming, used for modeling discrete knowledge about the game and the current situation. In particular, we make use of HEX programs, which are an extension of answer set programming (ASP) programs toward integration of external computation sources, such as 2-D physics simulation tools.
{"title":"Angry-HEX: An Artificial Player for Angry Birds Based on Declarative Knowledge Bases","authors":"Francesco Calimeri, Michael Fink, Stefano Germano, Giovambattista Ianni, Christoph Redl, Anton Wimmer","doi":"10.1109/TCIAIG.2015.2509600","DOIUrl":"https://doi.org/10.1109/TCIAIG.2015.2509600","url":null,"abstract":"This paper presents the Angry-HEX artificial intelligent agent that participated in the 2013 and 2014 Angry Birds Artificial Intelligence Competitions. The agent has been developed in the context of a joint project between the University of Calabria (UniCal) and the Vienna University of Technology (TU Vienna). The specific issues that arise when introducing artificial intelligence in a physics-based game are dealt with a combination of traditional imperative programming and declarative programming, used for modeling discrete knowledge about the game and the current situation. In particular, we make use of HEX programs, which are an extension of answer set programming (ASP) programs toward integration of external computation sources, such as 2-D physics simulation tools.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2015.2509600","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62593309","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 : 2016-06-01DOI: 10.1109/TCIAIG.2015.2478703
S. Schiffer, Maxim Jourenko, G. Lakemeyer
In this paper, we report on our entry for the AI Birds competition, where we designed, implemented, and evaluated an agent for the physics puzzle computer game Angry Birds. Our agent uses search and simulation to find appropriate parameters for launching birds. While there are other methods that focus on qualitative reasoning about physical systems we try to combine simulation and adjustable abstractions to efficiently traverse the possibly infinite search space. The agent features a hierarchical search scheme where different levels of abstractions are used. At any level, it uses simulation to rate subspaces that should be further explored in more detail on the next levels. We evaluate single components of our agent and we also compare the overall performance of different versions of our agent. We show that our approach yields a competitive solution on the standard set of levels.
{"title":"Akbaba—An Agent for the Angry Birds AI Challenge Based on Search and Simulation","authors":"S. Schiffer, Maxim Jourenko, G. Lakemeyer","doi":"10.1109/TCIAIG.2015.2478703","DOIUrl":"https://doi.org/10.1109/TCIAIG.2015.2478703","url":null,"abstract":"In this paper, we report on our entry for the AI Birds competition, where we designed, implemented, and evaluated an agent for the physics puzzle computer game Angry Birds. Our agent uses search and simulation to find appropriate parameters for launching birds. While there are other methods that focus on qualitative reasoning about physical systems we try to combine simulation and adjustable abstractions to efficiently traverse the possibly infinite search space. The agent features a hierarchical search scheme where different levels of abstractions are used. At any level, it uses simulation to rate subspaces that should be further explored in more detail on the next levels. We evaluate single components of our agent and we also compare the overall performance of different versions of our agent. We show that our approach yields a competitive solution on the standard set of levels.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2015.2478703","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62593323","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 : 2016-06-01DOI: 10.1109/TCIAIG.2016.2571560
Jochen Renz, R. Miikkulainen, Nathan R Sturtevant, M. Winands
The nine papers in this special section focus on the development of physics-based simulation video games (PBSG). The focus is on artificial intelligence for specific PBSGs competitions such as Angry Birds and computational pool, as well as on further developments of physics simulators in order to launch the next generation of PBSGs.
{"title":"Guest Editorial: Physics-Based Simulation Games","authors":"Jochen Renz, R. Miikkulainen, Nathan R Sturtevant, M. Winands","doi":"10.1109/TCIAIG.2016.2571560","DOIUrl":"https://doi.org/10.1109/TCIAIG.2016.2571560","url":null,"abstract":"The nine papers in this special section focus on the development of physics-based simulation video games (PBSG). The focus is on artificial intelligence for specific PBSGs competitions such as Angry Birds and computational pool, as well as on further developments of physics simulators in order to launch the next generation of PBSGs.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88986575","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 : 2016-06-01DOI: 10.1109/TCIAIG.2015.2462335
J. Landry, J. Dussault, É. Beaudry
In this work, we take a closer look at the difficulties inherent to the creation of AI for the game of Straight Billiards (14.1 continuous). We begin by establishing the key components that make this variant of billiards interesting in regard to past work on the game of Eight-Ball. We then address each of these components by decomposing the problem into two aspects: optimal control and planning. A new model for the optimal control of the cue ball to break clusters in between games is presented, as well as a model for the execution of defensive shots. We follow with a short discussion on the importance of planning carefully when only a few balls remain on the table and propose a planning approach based on an analysis of the table state to select the sequence of balls to pocket on the table. Results are finally presented and analyzed, followed by a discussion on future work.
{"title":"A Straight Approach to Planning for 14.1 Billiards","authors":"J. Landry, J. Dussault, É. Beaudry","doi":"10.1109/TCIAIG.2015.2462335","DOIUrl":"https://doi.org/10.1109/TCIAIG.2015.2462335","url":null,"abstract":"In this work, we take a closer look at the difficulties inherent to the creation of AI for the game of Straight Billiards (14.1 continuous). We begin by establishing the key components that make this variant of billiards interesting in regard to past work on the game of Eight-Ball. We then address each of these components by decomposing the problem into two aspects: optimal control and planning. A new model for the optimal control of the cue ball to break clusters in between games is presented, as well as a model for the execution of defensive shots. We follow with a short discussion on the importance of planning carefully when only a few balls remain on the table and propose a planning approach based on an analysis of the table state to select the sequence of balls to pocket on the table. Results are finally presented and analyzed, followed by a discussion on future work.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2015.2462335","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62593135","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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