Pub Date : 2009-09-07DOI: 10.1109/CIG.2009.5286467
Diego Perez Liebana, Gustavo Recio, Y. Sáez, P. I. Viñuela
Computational intelligence competitions have recently gained a lot of interest. These contests motivate and encourage researchers to participate on them, and to apply their work areas to specific games. During the last two years, one of the most popular competitions held on Computational Intelligence in Games conferences is the Car Racing Competition. This competition combines the fun of driving to win and the challenge of obtaining autonomous driving, which is known as a very difficult problem and faced by a lot of researches from different perspectives. For this competition, we have developed a controller with fuzzy rules and fuzzy sets for input and output, which were evolved using a genetic algorithm in order to optimise lap times, damage taken and out of track time. The design of this controller is explained in detail in this article, as well as the results obtained at the end of the contest.
{"title":"Evolving a fuzzy controller for a Car Racing Competition","authors":"Diego Perez Liebana, Gustavo Recio, Y. Sáez, P. I. Viñuela","doi":"10.1109/CIG.2009.5286467","DOIUrl":"https://doi.org/10.1109/CIG.2009.5286467","url":null,"abstract":"Computational intelligence competitions have recently gained a lot of interest. These contests motivate and encourage researchers to participate on them, and to apply their work areas to specific games. During the last two years, one of the most popular competitions held on Computational Intelligence in Games conferences is the Car Racing Competition. This competition combines the fun of driving to win and the challenge of obtaining autonomous driving, which is known as a very difficult problem and faced by a lot of researches from different perspectives. For this competition, we have developed a controller with fuzzy rules and fuzzy sets for input and output, which were evolved using a genetic algorithm in order to optimise lap times, damage taken and out of track time. The design of this controller is explained in detail in this article, as well as the results obtained at the end of the contest.","PeriodicalId":358795,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence and Games","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130855904","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 : 2009-09-07DOI: 10.1109/CIG.2009.5286470
M. Mehta, A. Corradini
Many researchers that develop full software applications in the broad field of natural language processing (NLP) typically implement their single system's components from scratch. While there is nothing wrong with such a methodology from an operational perspective, it typically results in a waste of time. Furthermore, it leads to a substantial diversion of the researchers' efforts from more conceptual and theoretical aspects that could be geared towards the advancement of the state-of-the-art in the field. These main drawbacks call for an implementation approach allowing components' reusability across domains and applications. In that respect, this paper presents an evaluation of a domain independent approach to natural language understanding (NLU) that we have been implementing over the last several years. We have successfully tested and used our approach in three different natural language interfaces to game-like applications, each with its own conversational domains.
{"title":"Evaluation of a domain independent approach to natural language processing for game-like user interfaces","authors":"M. Mehta, A. Corradini","doi":"10.1109/CIG.2009.5286470","DOIUrl":"https://doi.org/10.1109/CIG.2009.5286470","url":null,"abstract":"Many researchers that develop full software applications in the broad field of natural language processing (NLP) typically implement their single system's components from scratch. While there is nothing wrong with such a methodology from an operational perspective, it typically results in a waste of time. Furthermore, it leads to a substantial diversion of the researchers' efforts from more conceptual and theoretical aspects that could be geared towards the advancement of the state-of-the-art in the field. These main drawbacks call for an implementation approach allowing components' reusability across domains and applications. In that respect, this paper presents an evaluation of a domain independent approach to natural language understanding (NLU) that we have been implementing over the last several years. We have successfully tested and used our approach in three different natural language interfaces to game-like applications, each with its own conversational domains.","PeriodicalId":358795,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence and Games","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130931659","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 : 2009-09-07DOI: 10.1109/CIG.2009.5286466
E. Onieva, D. Pelta, J. Alonso, V. M. Montero, Joshué Pérez
This paper presents our approach to TORCS Car Racing Competition 2009, it is based on a complete modular architecture capable of driving automatically a car along a track with or without oppents. The architecture is composed of five simple modules being each one responsible for a basic aspect of car driving. The modules control gear shiftings, steer movements and pedals positions by using of simple functions meanwhile the allowed speed in a certain track segment is managed by a simple TSK fuzzy system.
{"title":"A modular parametric architecture for the TORCS racing engine","authors":"E. Onieva, D. Pelta, J. Alonso, V. M. Montero, Joshué Pérez","doi":"10.1109/CIG.2009.5286466","DOIUrl":"https://doi.org/10.1109/CIG.2009.5286466","url":null,"abstract":"This paper presents our approach to TORCS Car Racing Competition 2009, it is based on a complete modular architecture capable of driving automatically a car along a track with or without oppents. The architecture is composed of five simple modules being each one responsible for a basic aspect of car driving. The modules control gear shiftings, steer movements and pedals positions by using of simple functions meanwhile the allowed speed in a certain track segment is managed by a simple TSK fuzzy system.","PeriodicalId":358795,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence and Games","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131341806","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 : 2009-09-07DOI: 10.1109/CIG.2009.5286465
M. Ebner, Thorsten Tiede
Computational gaming requires the automatic generation of virtual opponents for different game levels. We have turned to artificial evolution to automatically generate such game players. In particular, we have used Genetic Programming to automatically evolve computer programs for computer gaming. With Genetic Programming, in theory, it is possible to generate any kind of program. The programs are not constrained as much as they are in other computational learning approaches, e.g. neural networks. We show how Genetic Programming improved upon a manually crafted race car driver (proportional controller). The open race car simulator TORCS was used to evaluate the virtual drivers.
{"title":"Evolving driving controllers using Genetic Programming","authors":"M. Ebner, Thorsten Tiede","doi":"10.1109/CIG.2009.5286465","DOIUrl":"https://doi.org/10.1109/CIG.2009.5286465","url":null,"abstract":"Computational gaming requires the automatic generation of virtual opponents for different game levels. We have turned to artificial evolution to automatically generate such game players. In particular, we have used Genetic Programming to automatically evolve computer programs for computer gaming. With Genetic Programming, in theory, it is possible to generate any kind of program. The programs are not constrained as much as they are in other computational learning approaches, e.g. neural networks. We show how Genetic Programming improved upon a manually crafted race car driver (proportional controller). The open race car simulator TORCS was used to evaluate the virtual drivers.","PeriodicalId":358795,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence and Games","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114672989","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 : 2009-09-07DOI: 10.1109/CIG.2009.5286478
L. DeLooze, Wesley R. Viner
This paper reports the results from training an intelligent agent to play the Ms. Pac-Man video game using variations of a fuzzy Q-learning algorithm. This approach allows us to address the nondeterministic aspects of the game as well as finding a successful self-learning or adaptive playing strategy. The strategy presented is a table based learning strategy, in which the intelligent agent analyzes the current situation of the game, stores various membership values for each of the several contributors to the situation (distance to closest pill, distance to closest power pill, and distance to closest ghost), and makes decisions based on these values.
{"title":"Fuzzy Q-learning in a nondeterministic environment: developing an intelligent Ms. Pac-Man agent","authors":"L. DeLooze, Wesley R. Viner","doi":"10.1109/CIG.2009.5286478","DOIUrl":"https://doi.org/10.1109/CIG.2009.5286478","url":null,"abstract":"This paper reports the results from training an intelligent agent to play the Ms. Pac-Man video game using variations of a fuzzy Q-learning algorithm. This approach allows us to address the nondeterministic aspects of the game as well as finding a successful self-learning or adaptive playing strategy. The strategy presented is a table based learning strategy, in which the intelligent agent analyzes the current situation of the game, stores various membership values for each of the several contributors to the situation (distance to closest pill, distance to closest power pill, and distance to closest ghost), and makes decisions based on these values.","PeriodicalId":358795,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence and Games","volume":"301 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123198071","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 : 2009-09-07DOI: 10.1109/CIG.2009.5286483
B. Weber, Michael Mateas
We present a data mining approach to opponent modeling in strategy games. Expert gameplay is learned by applying machine learning techniques to large collections of game logs. This approach enables domain independent algorithms to acquire domain knowledge and perform opponent modeling. Machine learning algorithms are applied to the task of detecting an opponent's strategy before it is executed and predicting when an opponent will perform strategic actions. Our approach involves encoding game logs as a feature vector representation, where each feature describes when a unit or building type is first produced. We compare our representation to a state lattice representation in perfect and imperfect information environments and the results show that our representation has higher predictive capabilities and is more tolerant of noise. We also discuss how to incorporate our data mining approach into a full game playing agent.
{"title":"A data mining approach to strategy prediction","authors":"B. Weber, Michael Mateas","doi":"10.1109/CIG.2009.5286483","DOIUrl":"https://doi.org/10.1109/CIG.2009.5286483","url":null,"abstract":"We present a data mining approach to opponent modeling in strategy games. Expert gameplay is learned by applying machine learning techniques to large collections of game logs. This approach enables domain independent algorithms to acquire domain knowledge and perform opponent modeling. Machine learning algorithms are applied to the task of detecting an opponent's strategy before it is executed and predicting when an opponent will perform strategic actions. Our approach involves encoding game logs as a feature vector representation, where each feature describes when a unit or building type is first produced. We compare our representation to a state lattice representation in perfect and imperfect information environments and the results show that our representation has higher predictive capabilities and is more tolerant of noise. We also discuss how to incorporate our data mining approach into a full game playing agent.","PeriodicalId":358795,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence and Games","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128074029","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 : 2009-09-07DOI: 10.1109/CIG.2009.5286495
P. Burrow, S. Lucas
This paper investigates various factors that affect the ability of a system to learn to play Ms. Pac-Man. For this study Ms. Pac-Man provides a game of appropriate complexity, and has the advantage that in recent years there have been many other papers published on systems that learn to play this game. The results indicate that Temporal Difference Learning (TDL) performs most reliably with a tabular function approximator, and that the reward structure chosen can have a dramatic impact on performance. When using a multi-layer perceptron as a function approximator, evolution outperforms TDL by a significant margin. Overall, the best results were obtained by evolving multi-layer perceptrons.
{"title":"Evolution versus Temporal Difference Learning for learning to play Ms. Pac-Man","authors":"P. Burrow, S. Lucas","doi":"10.1109/CIG.2009.5286495","DOIUrl":"https://doi.org/10.1109/CIG.2009.5286495","url":null,"abstract":"This paper investigates various factors that affect the ability of a system to learn to play Ms. Pac-Man. For this study Ms. Pac-Man provides a game of appropriate complexity, and has the advantage that in recent years there have been many other papers published on systems that learn to play this game. The results indicate that Temporal Difference Learning (TDL) performs most reliably with a tabular function approximator, and that the reward structure chosen can have a dramatic impact on performance. When using a multi-layer perceptron as a function approximator, evolution outperforms TDL by a significant margin. Overall, the best results were obtained by evolving multi-layer perceptrons.","PeriodicalId":358795,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence and Games","volume":"304 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127567302","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 : 2009-09-07DOI: 10.1109/CIG.2009.5286472
M. Kemmerling, Niels Ackermann, N. Beume, M. Preuss, Sebastian Uellenbeck, Wolfgang Walz
This paper presents a non-player character (NPC, bot) for the strategy game Diplomacy. The bot is able to communicate with other players and thus shows a human-like behavior. We investigate how far the playing abilities can be improved without corrupting the human-like behavior. Is there a trade-off at all or do these skills complement one another? Different versions of the bot are tested against other bots and humans which requires means to automatically measure believability. We derive such a measure after a general approach and apply it for monitoring the believability criterion while improving the playing strength of our bot.
{"title":"Is human-like and well playing contradictory for Diplomacy bots?","authors":"M. Kemmerling, Niels Ackermann, N. Beume, M. Preuss, Sebastian Uellenbeck, Wolfgang Walz","doi":"10.1109/CIG.2009.5286472","DOIUrl":"https://doi.org/10.1109/CIG.2009.5286472","url":null,"abstract":"This paper presents a non-player character (NPC, bot) for the strategy game Diplomacy. The bot is able to communicate with other players and thus shows a human-like behavior. We investigate how far the playing abilities can be improved without corrupting the human-like behavior. Is there a trade-off at all or do these skills complement one another? Different versions of the bot are tested against other bots and humans which requires means to automatically measure believability. We derive such a measure after a general approach and apply it for monitoring the believability criterion while improving the playing strength of our bot.","PeriodicalId":358795,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence and Games","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131035589","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 : 2009-09-07DOI: 10.1109/CIG.2009.5286510
Michael Mateas
Game AI research has successfully focused on improving the player experience, creating better tactical and strategic opponents, more convincing non-player characters, better path-finding approaches, and so on. However, as we create richer AI-based experiences for players, we can not forget authors. Human authors must be able to craft game experiences, creating the richness and nuances that make games compelling, while still taking advantage of the generativity and adaptability that is the hallmark of next generation game AI systems. This will require new AI-based authoring support tools. In this talk I will describe the authoring problem, and present a number of authoring support tool projects currently taking place in the Expressive Intelligence Studio at UC Santa Cruz.
{"title":"AI isn't just for players: AI-based authoring tools","authors":"Michael Mateas","doi":"10.1109/CIG.2009.5286510","DOIUrl":"https://doi.org/10.1109/CIG.2009.5286510","url":null,"abstract":"Game AI research has successfully focused on improving the player experience, creating better tactical and strategic opponents, more convincing non-player characters, better path-finding approaches, and so on. However, as we create richer AI-based experiences for players, we can not forget authors. Human authors must be able to craft game experiences, creating the richness and nuances that make games compelling, while still taking advantage of the generativity and adaptability that is the hallmark of next generation game AI systems. This will require new AI-based authoring support tools. In this talk I will describe the authoring problem, and present a number of authoring support tool projects currently taking place in the Expressive Intelligence Studio at UC Santa Cruz.","PeriodicalId":358795,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence and Games","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133151681","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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