A motivational framework for analyzing player and virtual agent behavior

Bostan, Barbaros

doi:10.1016/j.entcom.2010.09.002

Cited by 10 publications

(8 citation statements)

References 34 publications

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“…Found papers Included papers "player modeling" and "intrinsic motivation" 69 [11] "player modeling" and "competence" 132 [51,55,11] "player modeling" and "autonomy" 147 [11] "player modeling" and "relatedness" 23 [11] "player modeling" and "curiosity" 136 [27,11,53,65,25] "artificial intelligence" and "game testing" and "intrinsic motivation" 37 [20] "artificial intelligence" and "game testing" and "competence" 76 [20] "artificial intelligence" and "game testing" and "autonomy" 41 [20] "artificial intelligence" and "game testing" and "relatedness" 10 0 "artificial intelligence" and "game testing" and "curiosity" 78 [20] "player experience" and "intrinsic motivation" and "computational model" 32 [24,50] "player experience" and "competence" and "computational model" 53 [14,3,51,55,24,50] "player experience" and "autonomy" and "computational model" 48 [24] "player experience" and "relatedness" and "computational model" 11 [23,24] "player experience" and "curiosity" and "computational model" 51 [24,53,50] In addition to the explicit, algorithmic motivation models reviewed in this paper, simulated player agents can also utilize implicit, data-based models learned from real players. For example, the state-dependent probabilities of actions observed in real players have been included in the upper confidence bound (UCB) formula of Monte Carlo tree search [17,38].…”

Section: Searched Termsmentioning

confidence: 99%

“…Bostan [11] proposed a motivational framework for analyzing and predicting player and virtual agent behavior in games, based on 27 psychological needs from Murray's early theory [60]. Bostan provides formulas for determining the probability of various behaviors; thus, the framework can be used both for analyzing playtraces and synthesizing agent behavior.…”

Section: Other Motivations and Approachesmentioning

confidence: 99%

See 1 more Smart Citation

Review of Intrinsic Motivation in Simulation-based Game Testing

Roohi

Takatalo

Guckelsberger

et al. 2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

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This paper presents a review of intrinsic motivation in player modeling, with a focus on simulation-based game testing. Modern AI agents can learn to win many games; from a game testing perspective, a remaining research problem is how to model the aspects of human player behavior not explained by purely rational and goal-driven decision making. A major piece of this puzzle is constituted by intrinsic motivations, i.e., psychological needs that drive behavior without extrinsic reinforcement such as game score. We first review the common intrinsic motivations discussed in player psychology research and artificial intelligence, and then proceed to systematically review how the various motivations have been implemented in simulated player agents. Our work reveals that although motivations such as competence and curiosity have been studied in AI, work on utilizing them in simulation-based game testing is sparse, and other motivations such as social relatedness, immersion, and domination appear particularly underexplored.

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Section: Searched Termsmentioning

confidence: 99%

Section: Other Motivations and Approachesmentioning

confidence: 99%

Review of Intrinsic Motivation in Simulation-based Game Testing

Roohi

Takatalo

Guckelsberger

et al. 2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

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“…It should however be noted that a number of the studies cited above show that player matching should not necessarily be undertaken on the basis of skill level alone [9,6,8]. Instead, it is important to recognize that player motivations are multifaceted and are affected by a combination of psychological needs, behavioral patterns and personality traits [20]. Using survey and behavioral in-game data from the game Fallout: New Vegas, Chen et al show that scores and other aggregated features are of limited value in player matching.…”

Section: Literature and Hypothesesmentioning

confidence: 99%

More than skills: A novel matching proposal for multiplayer video games

Stroh-Maraun

Kaimann

Cox

2018

Entertainment Computing

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Multiplayer video games are high-involvement products with multiplatform and multiplayer characteristics which aim to enhance player retention by optimizing the matching of teams in accordance with their skills and attributes. However, relatively little academic research has been conducted into the ways in which player attributes can be used to optimize the formation of teams in multiplayer video games. Our study addresses this deficiency in the literature by analyzing a dataset from a popular online multiplayer game that includes historic behavioral data of 6.9 million players participating in 862,664 unique game rounds. We analyze the observable factors associated with longer duration of participation in each round, finding that player retention improves in the presence of player-versus-player combat, variety and heterogeneity. We also show that player retention diminishes as a result of the absence of particular role or vehicle use within a given round. Based on the findings of the analysis, we develop a novel approach called nested matching to assign players to teams with an optimal mixture of skills and inherent and complementary attributes.

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“…Goh et al [20] also introduce a work about the connection between the computer game design and strategies for mental health treatments of children and adolescents. One of the recent studies proposes a motivational framework to asses goal directed behaviours of the characters for both players and non-players in a computer game environment which leads to exploring the opportunities of using a Player and Agent Personality Database (PAPD) based on the same motivational framework for the design of virtual agents with personality in the computer game domains [24]. Children who suffer from language learning impairments (LLI) can benefit from computer games.…”

Section: The Use Of Computer Games For Cognitive Learningmentioning

confidence: 99%