Player modeling: Towards a common taxonomy

Machado, Marlos C.; Fantini, E.; Chaimowicz, Luiz

doi:10.1109/cgames.2011.6000359

Cited by 34 publications

(28 citation statements)

References 11 publications

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“…An evidence of that is that two different research groups developed, at the same time and independently, taxonomies for the field [4], [11]. This may be seen as a general need for a review and organization of the works in the literature.…”

Section: Related Workmentioning

confidence: 99%

“…In [4], besides the taxonomy, the authors also discuss the common techniques that have being used in Player Modeling, including machine learning. As mentioned, ML techniques can be divided in three different approaches: supervised, semi-supervised and unsupervised learning.…”

Section: Related Workmentioning

confidence: 99%

“…This modeling allows games to customize their AI to specific players, making the game experience more interesting. Among the players characteristics that can be modeled are preferences and knowledge [4]. This paper presents an automatic approach for preference modeling.…”

Section: Introductionmentioning

confidence: 99%

“…It is an important topic because, once we are able to identify how a player -virtual or not, likes to play, we are able to adapt the game to improve player's satisfaction. Some of the features we may adapt in the game are scenario [5], behavior and difficulty [4]. Moreover, Spronck and den Teuling argue that a preference model encompasses preferences and skills, and "has the potential to be much more useful than an action model", which is the most common approach for player modeling [6].…”

Section: Introductionmentioning

confidence: 99%

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A binary classification approach for automatic preference modeling of virtual agents in Civilization IV

Machado

Pappa

Chaimowicz

2012

2012 IEEE Conference on Computational Intelligence and Games (CIG)

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Abstract-Player Modeling tries to model players behaviors and characteristics during a game. When these are related to more abstract preferences, the process is normally called Preference Modeling. In this paper we infer Civilization IV's virtual agents preferences with classifiers based on support vector machines. Our vectors contain score indicators from agents gameplay, allowing us to predict preferences based on the indirect observations of actions. We model this task as a binary classification problem, allowing us to make more precise inference. In this sense, we leveraged previous approaches that also used kernel machines but relied on different preference levels. Using binary classification and parameter optimization, our method is able to predict some agents preferences with an accuracy of 100%. Moreover, it is also capable of generalizing to different agents, being able to predict preferences of agents that were not used in the training process.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A binary classification approach for automatic preference modeling of virtual agents in Civilization IV

Machado

Pappa

Chaimowicz

2012

2012 IEEE Conference on Computational Intelligence and Games (CIG)

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“…One way of doing this that is gradually receiving more attention from the scientific community is player modeling [Machado et al 2011], i.e., automatically adapting the game for each player. This modeling can be based on several characteristics such as knowledge, satisfaction or preferences.…”

Section: Introductionmentioning

confidence: 99%

Agents Behavior and Preferences Characterization in Civilization IV

Machado¹,

Rocha²,

Chaimowicz³

2011

2011 Brazilian Symposium on Games and Digital Entertainment

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Player Modeling is becoming an important feature in Digital Games. It basically consists in understanding and modeling the player characteristics and behaviors during the game and has been mainly used to improve the games artificial intelligence, making games more adaptable to different players. In this paper, we try to characterize the preference of the players using a novel approach in games: we use mathematical regressions to characterize players behavior, looking for functions that best fit these behaviors. Using AI controlled players in Civilization IV as a testbed, this characterization is performed by extracting game data (score and resources, for example) at the end of each turn and generating functions that characterize the data evolution during the game. We were able to obtain models that distinguish the agents preferences showing the effectiveness of this approach.

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