Pub Date : 2017-06-01DOI: 10.1109/TCIAIG.2016.2536689
L. F. Góes, Alysson Ribeiro da Silva, João Saffran, Alvaro Amorim, Celso França, Tiago Zaidan, Bernardo M. P. Olímpio, L. O. Alves, Hugo Morais, Shirley Luana, Carlos Martins
In recent years, online digital games have left behind the status of entertainment sources to become also professional electronic sports. Worldwide championships offer prizes up to millions of dollars for the best competitors and/or teams among different game categories such as digital collectible card games (DCCG), multiplayer online battle arena, etc. Hearthstone, by Blizzard Entertainment, is a DCCG that has an increasing number of players up to the millions. In this game, individual players compete in one-versus-one matches in alternating turns, until a player is defeated. The greatest challenge in this game is to build a deck of cards and a strategy to combine these cards in order to be competitive against other players without a priori knowledge about their decks and strategies. This is a daunting task that requires deep knowledge of each existing card and great amount of creativity to surprise adversaries in this very adaptive environment. This paper presents a computational system, called HoningStone, that automatically generates creative card combos based on the honing theory of creativity. Our experimental results show that HoningStone can generate combos that are more creative than a greedy randomized algorithm driven by a creativity metric.
{"title":"HoningStone: Building Creative Combos With Honing Theory for a Digital Card Game","authors":"L. F. Góes, Alysson Ribeiro da Silva, João Saffran, Alvaro Amorim, Celso França, Tiago Zaidan, Bernardo M. P. Olímpio, L. O. Alves, Hugo Morais, Shirley Luana, Carlos Martins","doi":"10.1109/TCIAIG.2016.2536689","DOIUrl":"https://doi.org/10.1109/TCIAIG.2016.2536689","url":null,"abstract":"In recent years, online digital games have left behind the status of entertainment sources to become also professional electronic sports. Worldwide championships offer prizes up to millions of dollars for the best competitors and/or teams among different game categories such as digital collectible card games (DCCG), multiplayer online battle arena, etc. Hearthstone, by Blizzard Entertainment, is a DCCG that has an increasing number of players up to the millions. In this game, individual players compete in one-versus-one matches in alternating turns, until a player is defeated. The greatest challenge in this game is to build a deck of cards and a strategy to combine these cards in order to be competitive against other players without a priori knowledge about their decks and strategies. This is a daunting task that requires deep knowledge of each existing card and great amount of creativity to surprise adversaries in this very adaptive environment. This paper presents a computational system, called HoningStone, that automatically generates creative card combos based on the honing theory of creativity. Our experimental results show that HoningStone can generate combos that are more creative than a greedy randomized algorithm driven by a creativity metric.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2016.2536689","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46636191","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 : 2017-06-01DOI: 10.1109/TCIAIG.2016.2535416
Daniel Jallov, S. Risi, J. Togelius
Neuroevolution [i.e., evolving artificial neural networks (ANNs) through evolutionary algorithms] has shown promise in evolving agents and robot controllers, which display complex behaviors and can adapt to their environments. These properties are also relevant to video games, since they can increase their longevity and replayability. However, the design of most current games precludes the use of any techniques which might yield unpredictable or even open-ended results. This paper describes the game EvoCommander, with the goal to further demonstrate the potential of neuroevolution in games. In EvoCommander the player incrementally evolves an arsenal of ANN-controlled behaviors (e.g., ranged attack, flee, etc.) for a simple robot that has to battle other player and computer controlled robots. The game introduces the novel game mechanic of “brain switching,” selecting which evolved neural network is active at any point during battle. Results from playtests indicate that brain switching is a promising new game mechanic, leading to players employing interesting different strategies when training their robots and when controlling them in battle.
{"title":"EvoCommander: A Novel Game Based on Evolving and Switching Between Artificial Brains","authors":"Daniel Jallov, S. Risi, J. Togelius","doi":"10.1109/TCIAIG.2016.2535416","DOIUrl":"https://doi.org/10.1109/TCIAIG.2016.2535416","url":null,"abstract":"Neuroevolution [i.e., evolving artificial neural networks (ANNs) through evolutionary algorithms] has shown promise in evolving agents and robot controllers, which display complex behaviors and can adapt to their environments. These properties are also relevant to video games, since they can increase their longevity and replayability. However, the design of most current games precludes the use of any techniques which might yield unpredictable or even open-ended results. This paper describes the game EvoCommander, with the goal to further demonstrate the potential of neuroevolution in games. In EvoCommander the player incrementally evolves an arsenal of ANN-controlled behaviors (e.g., ranged attack, flee, etc.) for a simple robot that has to battle other player and computer controlled robots. The game introduces the novel game mechanic of “brain switching,” selecting which evolved neural network is active at any point during battle. Results from playtests indicate that brain switching is a promising new game mechanic, leading to players employing interesting different strategies when training their robots and when controlling them in battle.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2016.2535416","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49251652","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 : 2017-06-01DOI: 10.1109/TCIAIG.2015.2511819
G. Bosc, Philip Tan, Jean-François Boulicaut, Chedy Raïssi, Mehdi Kaytoue-Uberall
Whereas purest strategic games such as Go and Chess seem timeless, the lifetime of a video game is short, influenced by popular culture, trends, boredom, and technological innovations. Even the important budget and developments allocated by editors cannot guarantee a timeless success. Instead, novelties and corrections are proposed to extend an inevitably bounded lifetime. Novelties can unexpectedly break the balance of a game, as players can discover unbalanced strategies that developers did not take into account. In the new context of electronic sports, an important challenge is to be able to detect game balance issues. In this paper, we consider real-time strategy (RTS) games and present an efficient pattern mining algorithm as a basic tool for game balance designers that enables one to search for unbalanced strategies in historical data through a knowledge discovery in databases (KDD) process. We experiment with our algorithm on StarCraft II historical data, played professionally as an electronic sport.
{"title":"A Pattern Mining Approach to Study Strategy Balance in RTS Games","authors":"G. Bosc, Philip Tan, Jean-François Boulicaut, Chedy Raïssi, Mehdi Kaytoue-Uberall","doi":"10.1109/TCIAIG.2015.2511819","DOIUrl":"https://doi.org/10.1109/TCIAIG.2015.2511819","url":null,"abstract":"Whereas purest strategic games such as Go and Chess seem timeless, the lifetime of a video game is short, influenced by popular culture, trends, boredom, and technological innovations. Even the important budget and developments allocated by editors cannot guarantee a timeless success. Instead, novelties and corrections are proposed to extend an inevitably bounded lifetime. Novelties can unexpectedly break the balance of a game, as players can discover unbalanced strategies that developers did not take into account. In the new context of electronic sports, an important challenge is to be able to detect game balance issues. In this paper, we consider real-time strategy (RTS) games and present an efficient pattern mining algorithm as a basic tool for game balance designers that enables one to search for unbalanced strategies in historical data through a knowledge discovery in databases (KDD) process. We experiment with our algorithm on StarCraft II historical data, played professionally as an electronic sport.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2015.2511819","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46518799","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 : 2017-06-01DOI: 10.1109/TCIAIG.2016.2520305
Michael Cook, S. Colton, J. Gow
Procedural content generation is generally viewed as a means to an end—a tool employed by designers to overcome technical problems or achieve a particular design goal. When we move from generating single parts of games to automating the entirety of their design, however, we find ourselves facing a far wider and more interesting set of problems than mere generation. When the designer of a game is a piece of software, we face questions about what it means to be a designer, about computational creativity, and about how to assess the growth of these automated game designers and the value of their output. Answering these questions can lead to new ideas in how to generate content procedurally, and produce systems that can further the cutting edge of game design. This paper describes work done to take an automated game designer and advance it towards being a member of a creative community. We outline extensions made to the system to give it more autonomy and creative independence, in order to strengthen claims that the software is acting creatively. We describe and reflect upon the software’s participation in the games community, including entering two game development contests, and show the opportunities and difficulties of such engagement. We consider methods for evaluating automated game designers as creative entities, and underline the need for automated game design to be a major frontier in future games research.
{"title":"The ANGELINA Videogame Design System—Part II","authors":"Michael Cook, S. Colton, J. Gow","doi":"10.1109/TCIAIG.2016.2520305","DOIUrl":"https://doi.org/10.1109/TCIAIG.2016.2520305","url":null,"abstract":"Procedural content generation is generally viewed as a means to an end—a tool employed by designers to overcome technical problems or achieve a particular design goal. When we move from generating single parts of games to automating the entirety of their design, however, we find ourselves facing a far wider and more interesting set of problems than mere generation. When the designer of a game is a piece of software, we face questions about what it means to be a designer, about computational creativity, and about how to assess the growth of these automated game designers and the value of their output. Answering these questions can lead to new ideas in how to generate content procedurally, and produce systems that can further the cutting edge of game design. This paper describes work done to take an automated game designer and advance it towards being a member of a creative community. We outline extensions made to the system to give it more autonomy and creative independence, in order to strengthen claims that the software is acting creatively. We describe and reflect upon the software’s participation in the games community, including entering two game development contests, and show the opportunities and difficulties of such engagement. We consider methods for evaluating automated game designers as creative entities, and underline the need for automated game design to be a major frontier in future games research.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2016.2520305","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42834445","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}
Automatically acquiring domain knowledge from professional game records, a kind of pattern learning, is an attractive and challenging issue in computer Go. This paper proposes a supervised learning method, by introducing a new generalized Bradley-Terry model, named Only-One-Victor, to learn patterns from game records. Basically, our algorithm applies the same idea with Elo rating algorithm, which considers each move in game records as a group of move patterns, and the selected move as the winner of a kind of competition among all groups on current board. However, being different from the generalized Bradley-Terry model for group competition used in Elo rating algorithm, Only-One-Victor model in our work simulates the process of making selection from a set of possible candidates by considering such process as a group of independent pairwise comparisons. We use a graph theory model to prove the correctness of Only-One-Victor model. In addition, we also apply the Minorization-Maximization (MM) to solve the optimization task. Therefore, our algorithm still enjoys many computational advantages of Elo rating algorithm, such as the scalability with high dimensional feature space. With the training set containing 115,832 moves and the same feature setting, the results of our experiments show that Only-One-Victor outperforms Elo rating, a well-known best supervised pattern learning method.
{"title":"Only-One-Victor Pattern Learning in Computer Go","authors":"Jiao Wang, Chenjun Xiao, Tan Zhu, Chu-Hsuan Hsueh, Wen-Jie Tseng, I-Chen Wu","doi":"10.1109/TCIAIG.2015.2504108","DOIUrl":"https://doi.org/10.1109/TCIAIG.2015.2504108","url":null,"abstract":"Automatically acquiring domain knowledge from professional game records, a kind of pattern learning, is an attractive and challenging issue in computer Go. This paper proposes a supervised learning method, by introducing a new generalized Bradley-Terry model, named Only-One-Victor, to learn patterns from game records. Basically, our algorithm applies the same idea with Elo rating algorithm, which considers each move in game records as a group of move patterns, and the selected move as the winner of a kind of competition among all groups on current board. However, being different from the generalized Bradley-Terry model for group competition used in Elo rating algorithm, Only-One-Victor model in our work simulates the process of making selection from a set of possible candidates by considering such process as a group of independent pairwise comparisons. We use a graph theory model to prove the correctness of Only-One-Victor model. In addition, we also apply the Minorization-Maximization (MM) to solve the optimization task. Therefore, our algorithm still enjoys many computational advantages of Elo rating algorithm, such as the scalability with high dimensional feature space. With the training set containing 115,832 moves and the same feature setting, the results of our experiments show that Only-One-Victor outperforms Elo rating, a well-known best supervised pattern learning method.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2015.2504108","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48691366","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 : 2017-03-01DOI: 10.1109/TCIAIG.2015.2491611
Richard Mealing, J. Shapiro
We consider the problem of learning an effective strategy online in a hidden information game against an opponent with a changing strategy. We want to model and exploit the opponent and make three proposals to do this; first, to infer its hidden information using an expectation–maximization (EM) algorithm; second, to predict its actions using a sequence prediction method; and third, to simulate games between our agent and our opponent model in-between games against the opponent. Our approach does not require knowledge outside the rules of the game, and does not assume that the opponent’s strategy is stationary. Experiments in simplified poker games show that it increases the average payoff per game of a state-of-the-art no-regret learning algorithm.
{"title":"Opponent Modeling by Expectation–Maximization and Sequence Prediction in Simplified Poker","authors":"Richard Mealing, J. Shapiro","doi":"10.1109/TCIAIG.2015.2491611","DOIUrl":"https://doi.org/10.1109/TCIAIG.2015.2491611","url":null,"abstract":"We consider the problem of learning an effective strategy online in a hidden information game against an opponent with a changing strategy. We want to model and exploit the opponent and make three proposals to do this; first, to infer its hidden information using an expectation–maximization (EM) algorithm; second, to predict its actions using a sequence prediction method; and third, to simulate games between our agent and our opponent model in-between games against the opponent. Our approach does not require knowledge outside the rules of the game, and does not assume that the opponent’s strategy is stationary. Experiments in simplified poker games show that it increases the average payoff per game of a state-of-the-art no-regret learning algorithm.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2015.2491611","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49516694","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 : 2017-03-01DOI: 10.1109/TCIAIG.2015.2505240
Piotr Beling
Partition search is a form of game search, proposed by Matthew L. Ginsberg in 1996, who wrote that the method “incorporates dependency analysis, allowing substantial reductions in the portion of the tree that needs to be expanded.” In this paper, some improvements of the partition search algorithm are proposed. The effectiveness of the most important extension we contribute, which we call local partition search, has been verified experimentally. The results obtained (which we present in the paper) show that using this extension, leads, in the case of bridge, to a significant reduction (almost by half) of the search tree size and calculation time. Another extension we proposed allows for more effective usage of the transposition table (using it to narrow the search window or by cutting more than one entry). Additionally, we contribute a formal proof of the correctness of all presented partition search variants. We draw conclusions from it about a possible generalization of partition search by making the definition of a partition system less restrictive. We also provide a formal definition of a partition system for the double dummy bridge.
{"title":"Partition Search Revisited","authors":"Piotr Beling","doi":"10.1109/TCIAIG.2015.2505240","DOIUrl":"https://doi.org/10.1109/TCIAIG.2015.2505240","url":null,"abstract":"Partition search is a form of game search, proposed by Matthew L. Ginsberg in 1996, who wrote that the method “incorporates dependency analysis, allowing substantial reductions in the portion of the tree that needs to be expanded.” In this paper, some improvements of the partition search algorithm are proposed. The effectiveness of the most important extension we contribute, which we call local partition search, has been verified experimentally. The results obtained (which we present in the paper) show that using this extension, leads, in the case of bridge, to a significant reduction (almost by half) of the search tree size and calculation time. Another extension we proposed allows for more effective usage of the transposition table (using it to narrow the search window or by cutting more than one entry). Additionally, we contribute a formal proof of the correctness of all presented partition search variants. We draw conclusions from it about a possible generalization of partition search by making the definition of a partition system less restrictive. We also provide a formal definition of a partition system for the double dummy bridge.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2015.2505240","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45648152","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 : 2017-03-01DOI: 10.1109/TCIAIG.2015.2490339
Koray Balci, A. A. Salah
Artificial intelligence and machine learning techniques are not only useful for creating plausible behaviors for interactive game elements, but also for the analysis of the players to provide a better gaming environment. In this paper, we propose a novel framework for automatic classification of player complaints in a social gaming platform. We use features that describe both parties of the complaint (namely, the accuser and the suspect), as well as interaction features of the game itself. The proposed classification approach, based on gradient boosting machines, is tested on the COPA Database of 100 000 unique users and 800 000 individual games. We advance the state of the art in this challenging problem.
{"title":"Automatic Classification of Player Complaints in Social Games","authors":"Koray Balci, A. A. Salah","doi":"10.1109/TCIAIG.2015.2490339","DOIUrl":"https://doi.org/10.1109/TCIAIG.2015.2490339","url":null,"abstract":"Artificial intelligence and machine learning techniques are not only useful for creating plausible behaviors for interactive game elements, but also for the analysis of the players to provide a better gaming environment. In this paper, we propose a novel framework for automatic classification of player complaints in a social gaming platform. We use features that describe both parties of the complaint (namely, the accuser and the suspect), as well as interaction features of the game itself. The proposed classification approach, based on gradient boosting machines, is tested on the COPA Database of 100 000 unique users and 800 000 individual games. We advance the state of the art in this challenging problem.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2015.2490339","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62593086","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 : 2017-03-01DOI: 10.1109/TCIAIG.2015.2394780
Jiawei Li, G. Kendall
Hyperheuristics have been successfully applied in solving a variety of computational search problems. In this paper, we investigate a hyperheuristic methodology to generate adaptive strategies for games. Based on a set of low-level heuristics (or strategies), a hyperheuristic game player can generate strategies which adapt to both the behavior of the co-players and the game dynamics. By using a simple heuristic selection mechanism, a number of existing heuristics for specialized games can be integrated into an automated game player. As examples, we develop hyperheuristic game players for three games: iterated prisoner's dilemma, repeated Goofspiel and the competitive traveling salesmen problem. The results demonstrate that a hyperheuristic game player outperforms the low-level heuristics, when used individually in game playing and it can generate adaptive strategies even if the low-level heuristics are deterministic. This methodology provides an efficient way to develop new strategies for games based on existing strategies.
{"title":"A Hyperheuristic Methodology to Generate Adaptive Strategies for Games","authors":"Jiawei Li, G. Kendall","doi":"10.1109/TCIAIG.2015.2394780","DOIUrl":"https://doi.org/10.1109/TCIAIG.2015.2394780","url":null,"abstract":"Hyperheuristics have been successfully applied in solving a variety of computational search problems. In this paper, we investigate a hyperheuristic methodology to generate adaptive strategies for games. Based on a set of low-level heuristics (or strategies), a hyperheuristic game player can generate strategies which adapt to both the behavior of the co-players and the game dynamics. By using a simple heuristic selection mechanism, a number of existing heuristics for specialized games can be integrated into an automated game player. As examples, we develop hyperheuristic game players for three games: iterated prisoner's dilemma, repeated Goofspiel and the competitive traveling salesmen problem. The results demonstrate that a hyperheuristic game player outperforms the low-level heuristics, when used individually in game playing and it can generate adaptive strategies even if the low-level heuristics are deterministic. This methodology provides an efficient way to develop new strategies for games based on existing strategies.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2015.2394780","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45604973","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 : 2017-03-01DOI: 10.1109/TCIAIG.2015.2494599
Xiangyang Huang, Shudong Zhang, Yuanyuan Shang, Wei-gong Zhang, Jie Liu
The ability to reason about and respond to their own emotional states can enhance the believability of Non-Player Characters (NPCs). In this paper, we use a Partially Observable Markov Decision Process (POMDP)-based framework to model emotion over time. A two-level appraisal model, involving quick and reactive vs. slow and deliberate appraisals, is proposed for the creation of affective autonomous characters based on POMDPs, wherein the probability of goal satisfaction is used in an appraisal and reappraisal process for emotion generation. We not only extend Probabilistic Computation Tree Logic (PCTL) for reasoning about the properties of emotional states based on POMDPs but also illustrate how four reactive (primary) emotions and nine deliberate (secondary) emotions can be derived by combining PCTL with the belief-desire theory of emotion. The results of an empirical study suggest that the proposed model can be used to create characters that appear to be more believable and more intelligent.
{"title":"Creating Affective Autonomous Characters Using Planning in Partially Observable Stochastic Domains","authors":"Xiangyang Huang, Shudong Zhang, Yuanyuan Shang, Wei-gong Zhang, Jie Liu","doi":"10.1109/TCIAIG.2015.2494599","DOIUrl":"https://doi.org/10.1109/TCIAIG.2015.2494599","url":null,"abstract":"The ability to reason about and respond to their own emotional states can enhance the believability of Non-Player Characters (NPCs). In this paper, we use a Partially Observable Markov Decision Process (POMDP)-based framework to model emotion over time. A two-level appraisal model, involving quick and reactive vs. slow and deliberate appraisals, is proposed for the creation of affective autonomous characters based on POMDPs, wherein the probability of goal satisfaction is used in an appraisal and reappraisal process for emotion generation. We not only extend Probabilistic Computation Tree Logic (PCTL) for reasoning about the properties of emotional states based on POMDPs but also illustrate how four reactive (primary) emotions and nine deliberate (secondary) emotions can be derived by combining PCTL with the belief-desire theory of emotion. The results of an empirical study suggest that the proposed model can be used to create characters that appear to be more believable and more intelligent.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2015.2494599","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49555165","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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