Pub Date : 2017-12-01DOI: 10.1109/TCIAIG.2016.2642158
G. Martínez-Arellano, R. Cant, D. Woods
This paper proposes a character generation approach for the M.U.G.E.N. fighting game that can create engaging AI characters using a computationally cheap process without the intervention of the expert developer. The approach uses a genetic programming algorithm that refines randomly generated character strategies into better ones using tournament selection. The generated AI characters were tested by 27 human players and were rated according to results, perceived difficulty and how engaging the gameplay was. The main advantages of this procedure are that no prior knowledge of how to code the strategies of the AI character is needed and there is no need to interact with the internal code of the game. In addition, the procedure is capable of creating a wide diversity of players with different strategic skills, which could be potentially used as a starting point to a further adaptive process.
{"title":"Creating AI Characters for Fighting Games Using Genetic Programming","authors":"G. Martínez-Arellano, R. Cant, D. Woods","doi":"10.1109/TCIAIG.2016.2642158","DOIUrl":"https://doi.org/10.1109/TCIAIG.2016.2642158","url":null,"abstract":"This paper proposes a character generation approach for the M.U.G.E.N. fighting game that can create engaging AI characters using a computationally cheap process without the intervention of the expert developer. The approach uses a genetic programming algorithm that refines randomly generated character strategies into better ones using tournament selection. The generated AI characters were tested by 27 human players and were rated according to results, perceived difficulty and how engaging the gameplay was. The main advantages of this procedure are that no prior knowledge of how to code the strategies of the AI character is needed and there is no need to interact with the internal code of the game. In addition, the procedure is capable of creating a wide diversity of players with different strategic skills, which could be potentially used as a starting point to a further adaptive process.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2016.2642158","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44519444","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 : 2017-12-01DOI: 10.1109/TCIAIG.2016.2623560
Sam Snodgrass, Santiago Ontañón
Procedural content generation has become a popular research topic in recent years. However, most content generation systems are specialized to a single game. We are interested in methods that can generate content for a wide variety of games without a game-specific algorithm design. Statistical approaches are a promising avenue for such generators and, more specifically, map generators. In this paper, we explore Markov models as a means of modeling and generating content for multiple domains. We apply our Markov models to Super Mario Bros., Loderunner , and Kid Icarus in order to determine how well our models perform in terms of the playability of the content generated, the expressive ranges of the models, and the effects of training data on those expressive ranges.
{"title":"Learning to Generate Video Game Maps Using Markov Models","authors":"Sam Snodgrass, Santiago Ontañón","doi":"10.1109/TCIAIG.2016.2623560","DOIUrl":"https://doi.org/10.1109/TCIAIG.2016.2623560","url":null,"abstract":"Procedural content generation has become a popular research topic in recent years. However, most content generation systems are specialized to a single game. We are interested in methods that can generate content for a wide variety of games without a game-specific algorithm design. Statistical approaches are a promising avenue for such generators and, more specifically, map generators. In this paper, we explore Markov models as a means of modeling and generating content for multiple domains. We apply our Markov models to Super Mario Bros., Loderunner , and Kid Icarus in order to determine how well our models perform in terms of the playability of the content generated, the expressive ranges of the models, and the effects of training data on those expressive ranges.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2016.2623560","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48083947","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 : 2017-12-01DOI: 10.1109/TCIAIG.2016.2593977
M. Fendt, R. Young
Character intention revision is an essential component of stories, but it has yet to be incorporated into story generation systems. However, intentionality, one component of intention revision, has been explored in both narrative generation and logical formalisms. The intention revision in storytelling (IRIS) system adopts the belief/desire/intention framework of intentionality from logical formalisms and combines it with preexisting concepts of intentionality in narrative. IRIS also introduces the crucial concept of intention revision for the protagonist of the story. The IRIS system uses its generative power alongside psychological and narrative models of suspense to computationally create suspenseful stories. It has been used in the creation of suspenseful noninteractive text stories and an interactive text-based video game.
{"title":"Leveraging Intention Revision in Narrative Planning to Create Suspenseful Stories","authors":"M. Fendt, R. Young","doi":"10.1109/TCIAIG.2016.2593977","DOIUrl":"https://doi.org/10.1109/TCIAIG.2016.2593977","url":null,"abstract":"Character intention revision is an essential component of stories, but it has yet to be incorporated into story generation systems. However, intentionality, one component of intention revision, has been explored in both narrative generation and logical formalisms. The intention revision in storytelling (IRIS) system adopts the belief/desire/intention framework of intentionality from logical formalisms and combines it with preexisting concepts of intentionality in narrative. IRIS also introduces the crucial concept of intention revision for the protagonist of the story. The IRIS system uses its generative power alongside psychological and narrative models of suspense to computationally create suspenseful stories. It has been used in the creation of suspenseful noninteractive text stories and an interactive text-based video game.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2016.2593977","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47699986","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 : 2017-12-01DOI: 10.1109/TCIAIG.2016.2565558
Eun-Youn Kim, D. Ashlock
The iterated prisoner's dilemma is a simultaneous two-player game widely used in studies on cooperation and conflict. Recent research has demonstrated that a number of factors change the behavior of evolved agents in a manner not consistent with controlled studies. This study extends a preliminary exploration of the impact of changing the level of computational or informational resources available to game playing agents on their ensemble behavior. Both these categories of information are shown to have an impact on agent behavior. Four representations are studied: lookup tables, Markov chains, finite-state machines, and feed-forward neural nets. An assessment tool called the play profile is used to demonstrate that both the cooperativeness and the change in cooperativeness over evolutionary time are substantially different for different resource levels within a representational type. Lookup tables and neural nets are found to change the least when the resource levels they are presented with are varied, while Markov chains vary the most. Available internal resources are also found to change the competitive ability of agents as well as the rate at which they become cooperative as evolution proceeds.
{"title":"Changing Resources Available to Game Playing Agents: Another Relevant Design Factor in Agent Experiments","authors":"Eun-Youn Kim, D. Ashlock","doi":"10.1109/TCIAIG.2016.2565558","DOIUrl":"https://doi.org/10.1109/TCIAIG.2016.2565558","url":null,"abstract":"The iterated prisoner's dilemma is a simultaneous two-player game widely used in studies on cooperation and conflict. Recent research has demonstrated that a number of factors change the behavior of evolved agents in a manner not consistent with controlled studies. This study extends a preliminary exploration of the impact of changing the level of computational or informational resources available to game playing agents on their ensemble behavior. Both these categories of information are shown to have an impact on agent behavior. Four representations are studied: lookup tables, Markov chains, finite-state machines, and feed-forward neural nets. An assessment tool called the play profile is used to demonstrate that both the cooperativeness and the change in cooperativeness over evolutionary time are substantially different for different resource levels within a representational type. Lookup tables and neural nets are found to change the least when the resource levels they are presented with are varied, while Markov chains vary the most. Available internal resources are also found to change the competitive ability of agents as well as the rate at which they become cooperative as evolution proceeds.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2016.2565558","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44585904","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 : 2017-12-01DOI: 10.1109/TCIAIG.2016.2593000
R. Cardoso, A. Gomes, M. Freire
Representing, manipulating, and inferring trust from the user point of view certainly is a grand challenge in virtual worlds, including online games. When someone meets an unknown individual, the question is “Can I trust him/her or not?” This requires the user to have access to a representation of trust about others, as well as a set of operators to undertake inference about the trustability of other users/players. In this paper, we employ a trust representation generated from in-world data in order to feed individual trust decisions. To achieve that purpose, we assume that such a representation of trust already exists; in fact, it was proposed in another paper of ours. Thus, the focus here is on the trust mechanisms required to infer trustability of other users/players. More specifically, we use an individual trust representation deployed as a trust network as base to the inference mechanism that employs two subjective logic operators (consensus and discount) to automatically derive trust decisions. The proposed trust inference system has been validated through OpenSimulator scenarios, which has led to a 5% increase on trustability of avatars in relation to the reference scenario (without trust).
{"title":"A User Trust System for Online Games—Part II: A Subjective Logic Approach for Trust Inference","authors":"R. Cardoso, A. Gomes, M. Freire","doi":"10.1109/TCIAIG.2016.2593000","DOIUrl":"https://doi.org/10.1109/TCIAIG.2016.2593000","url":null,"abstract":"Representing, manipulating, and inferring trust from the user point of view certainly is a grand challenge in virtual worlds, including online games. When someone meets an unknown individual, the question is “Can I trust him/her or not?” This requires the user to have access to a representation of trust about others, as well as a set of operators to undertake inference about the trustability of other users/players. In this paper, we employ a trust representation generated from in-world data in order to feed individual trust decisions. To achieve that purpose, we assume that such a representation of trust already exists; in fact, it was proposed in another paper of ours. Thus, the focus here is on the trust mechanisms required to infer trustability of other users/players. More specifically, we use an individual trust representation deployed as a trust network as base to the inference mechanism that employs two subjective logic operators (consensus and discount) to automatically derive trust decisions. The proposed trust inference system has been validated through OpenSimulator scenarios, which has led to a 5% increase on trustability of avatars in relation to the reference scenario (without trust).","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2016.2593000","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43128409","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 : 2017-12-01DOI: 10.1109/TCIAIG.2016.2604923
J. Olivito, J. Resano, J. L. Briz
Board games applications usually offer a great user experience when running on desktop computers. Powerful high-performance processors working without energy restrictions successfully deal with the exploration of large game trees, delivering strong play to satisfy demanding users. However, nowadays, more and more game players are running these games on smartphones and tablets, where the lower computational power and limited power budget yield a much weaker play. Recent systems-on-a-chip include programmable logic tightly coupled with general-purpose processors enabling the inclusion of custom accelerators for any application to improve both performance and energy efficiency. In this paper, we analyze the benefits of partitioning the artificial intelligence of board games into software and hardware. We have chosen as case studies three popular and complex board games, Reversi, Blokus, and Connect6. The designs analyzed include hardware accelerators for board processing, which improve performance and energy efficiency by an order of magnitude leading to much stronger and battery-aware applications. The results demonstrate that the use of hardware/software codesign to develop board games allows sustaining or even improving the user experience across platforms while keeping power and energy low.
{"title":"Accelerating Board Games Through Hardware/Software Codesign","authors":"J. Olivito, J. Resano, J. L. Briz","doi":"10.1109/TCIAIG.2016.2604923","DOIUrl":"https://doi.org/10.1109/TCIAIG.2016.2604923","url":null,"abstract":"Board games applications usually offer a great user experience when running on desktop computers. Powerful high-performance processors working without energy restrictions successfully deal with the exploration of large game trees, delivering strong play to satisfy demanding users. However, nowadays, more and more game players are running these games on smartphones and tablets, where the lower computational power and limited power budget yield a much weaker play. Recent systems-on-a-chip include programmable logic tightly coupled with general-purpose processors enabling the inclusion of custom accelerators for any application to improve both performance and energy efficiency. In this paper, we analyze the benefits of partitioning the artificial intelligence of board games into software and hardware. We have chosen as case studies three popular and complex board games, Reversi, Blokus, and Connect6. The designs analyzed include hardware accelerators for board processing, which improve performance and energy efficiency by an order of magnitude leading to much stronger and battery-aware applications. The results demonstrate that the use of hardware/software codesign to develop board games allows sustaining or even improving the user experience across platforms while keeping power and energy low.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2016.2604923","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43517142","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 : 2017-12-01DOI: 10.1109/TCIAIG.2016.2570006
Neesha Desai, Richard Zhao, D. Szafron
Gender in video games is a popular topic. However, the focus is usually on how gender is portrayed within games. In this paper, we examine the effects of players' gender on the perception of virtual character behavior and emotion based on the results of two user studies involving story-based games. The first study compared players' perception of virtual character behaviors. We analyzed perceived differences both by gender and by gaming experience. In this study, we found that female gamers were more appreciative of complex behaviors than male gamers. In the second study, we examined the influence of gender on player' ability to identify the emotion being displayed by a virtual character. We found that most emotions were identified comparably, with the exception of anger. Female players were significantly better at identifying angry characters compared to male players. We also investigated any perception differences between emotions expressed by male and female virtual characters, but we did not identify any statistically significant differences. Overall, the studies suggest that there are differences in how male and female players perceive virtual characters, and if game designers want players to perceive these characters in a certain way, they should consider the gender of targeted players.
{"title":"Effects of Gender on Perception and Interpretation of Video Game Character Behavior and Emotion","authors":"Neesha Desai, Richard Zhao, D. Szafron","doi":"10.1109/TCIAIG.2016.2570006","DOIUrl":"https://doi.org/10.1109/TCIAIG.2016.2570006","url":null,"abstract":"Gender in video games is a popular topic. However, the focus is usually on how gender is portrayed within games. In this paper, we examine the effects of players' gender on the perception of virtual character behavior and emotion based on the results of two user studies involving story-based games. The first study compared players' perception of virtual character behaviors. We analyzed perceived differences both by gender and by gaming experience. In this study, we found that female gamers were more appreciative of complex behaviors than male gamers. In the second study, we examined the influence of gender on player' ability to identify the emotion being displayed by a virtual character. We found that most emotions were identified comparably, with the exception of anger. Female players were significantly better at identifying angry characters compared to male players. We also investigated any perception differences between emotions expressed by male and female virtual characters, but we did not identify any statistically significant differences. Overall, the studies suggest that there are differences in how male and female players perceive virtual characters, and if game designers want players to perceive these characters in a certain way, they should consider the gender of targeted players.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2016.2570006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46781353","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 : 2017-12-01DOI: 10.1109/TCIAIG.2016.2620060
Bikramjit Banerjee, Caleb E. Davis
Multiagent path finding is the problem of finding paths for a set of agents—one for each agent—in a graph that the agents navigate simultaneously. Agents must navigate from their individual start to goal vertices without any collision. We argue that the prevalent treatment of path conflicts in the literature is incomplete for applications, such as computer games and crowd simulations, and extend the definition of path conflicts to accommodate cases where agents persist in their intermediate locations and even after reaching their goals. We show that an existing algorithm, conflict-based search (CBS), can be extended to handle these cases while preserving its optimality. Experiments show that our variant of CBS generates fewer nodes and runs faster than a competing algorithm.
{"title":"Multiagent Path Finding With Persistence Conflicts","authors":"Bikramjit Banerjee, Caleb E. Davis","doi":"10.1109/TCIAIG.2016.2620060","DOIUrl":"https://doi.org/10.1109/TCIAIG.2016.2620060","url":null,"abstract":"Multiagent path finding is the problem of finding paths for a set of agents—one for each agent—in a graph that the agents navigate simultaneously. Agents must navigate from their individual start to goal vertices without any collision. We argue that the prevalent treatment of path conflicts in the literature is incomplete for applications, such as computer games and crowd simulations, and extend the definition of path conflicts to accommodate cases where agents persist in their intermediate locations and even after reaching their goals. We show that an existing algorithm, conflict-based search (CBS), can be extended to handle these cases while preserving its optimality. Experiments show that our variant of CBS generates fewer nodes and runs faster than a competing algorithm.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2016.2620060","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44547100","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 : 2017-12-01DOI: 10.1109/TCIAIG.2016.2575823
Josep Valls-Vargas, Jichen Zhu, Santiago Ontañón
In this paper, we present our method for automatically extracting narrative information of characters and their narrative roles from natural language stories. In our corpus of 15 unannotated folk tales, our Voz system identifies 87% of the characters in the stories and correctly assigns 68% of the character roles. To better understand the sources of error in our system, we present an analytical methodology to study how the error is introduced by different modules and how it propagates through the pipeline. This methodology allows us to identify the bottleneck with the largest impact on the final error, which might be different from the module with the largest individual error in isolation. Our methodology can be applied to a wide variety of similar information extraction pipelines.
{"title":"Error Analysis in an Automated Narrative Information Extraction Pipeline","authors":"Josep Valls-Vargas, Jichen Zhu, Santiago Ontañón","doi":"10.1109/TCIAIG.2016.2575823","DOIUrl":"https://doi.org/10.1109/TCIAIG.2016.2575823","url":null,"abstract":"In this paper, we present our method for automatically extracting narrative information of characters and their narrative roles from natural language stories. In our corpus of 15 unannotated folk tales, our Voz system identifies 87% of the characters in the stories and correctly assigns 68% of the character roles. To better understand the sources of error in our system, we present an analytical methodology to study how the error is introduced by different modules and how it propagates through the pipeline. This methodology allows us to identify the bottleneck with the largest impact on the final error, which might be different from the module with the largest individual error in isolation. Our methodology can be applied to a wide variety of similar information extraction pipelines.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2016.2575823","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47172189","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 : 2017-09-01DOI: 10.1109/TCIAIG.2016.2556281
D. Luh, Elena Carolina Li, Chia-Chen Dai
Online pet games emphasize the enjoyment of raising a pet, social interaction, and expression of the personality. Players often obtain a sense of satisfaction and develop companionship with their pets. However, the question pertinent to why people continue playing with online pets is still unclear. The answer of this question can help developers improve game designs. The flow experience is a factor contributing to players’ willingness to engage in a gaming activity. Therefore, the purpose of this study is to adopt the concept of flow to help understand why people enjoy playing online pet games. In this study, the authors analyzed flow factors and identified crucial game elements that might elicit a flow experience by means of a questionnaire survey. A total of 180 valid questionnaires were collected, and the results demonstrated that role playing is an important flow factor for online pet games. In addition, social interaction, personality design, and creativity behavior are crucial game elements contributing to players’ willingness to continue playing with online pets.
{"title":"Game Factors Influencing Players to Continue Playing Online Pets","authors":"D. Luh, Elena Carolina Li, Chia-Chen Dai","doi":"10.1109/TCIAIG.2016.2556281","DOIUrl":"https://doi.org/10.1109/TCIAIG.2016.2556281","url":null,"abstract":"Online pet games emphasize the enjoyment of raising a pet, social interaction, and expression of the personality. Players often obtain a sense of satisfaction and develop companionship with their pets. However, the question pertinent to why people continue playing with online pets is still unclear. The answer of this question can help developers improve game designs. The flow experience is a factor contributing to players’ willingness to engage in a gaming activity. Therefore, the purpose of this study is to adopt the concept of flow to help understand why people enjoy playing online pet games. In this study, the authors analyzed flow factors and identified crucial game elements that might elicit a flow experience by means of a questionnaire survey. A total of 180 valid questionnaires were collected, and the results demonstrated that role playing is an important flow factor for online pet games. In addition, social interaction, personality design, and creativity behavior are crucial game elements contributing to players’ willingness to continue playing with online pets.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2016.2556281","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45790323","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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