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Player Classification Using a Meta-Clustering Approach
"... Player classification has recently become a key aspect of game design in areas such as adaptive game systems, player behaviour prediction, player tutoring and non-player character design. Past research has focused on the design of hierarchical, preferencebased and probabilistic models aimed at model ..."
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Player classification has recently become a key aspect of game design in areas such as adaptive game systems, player behaviour prediction, player tutoring and non-player character design. Past research has focused on the design of hierarchical, preferencebased and probabilistic models aimed at modelling players' behaviour. We propose a meta-classification approach that breaks the clustering of gameplay mixed data into three levels of analysis. The first level uses dimensionality reduction and partitional clustering of aggregate game data in an action/skillbased classification. The second level applies similarity-based clustering of action sequences to group players according to their preferences. For this we propose a new approach which uses Rubner’s Earth Mover’s Distance (EMD) as a similarity metric to compare histograms of players ’ game world explorations. The third level applies a combination of social network analysis metrics, such as shortest path length, to social data to find clusters in the players ' social network. We test our approach in a gameplay dataset from a freely available first-person social hunting game.
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, 2013
"... Improving meIRL-based prediction models in video games using general behaviour classification ..."
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Improving meIRL-based prediction models in video games using general behaviour classification
A Generic Method for Classification of Player Behavior
"... Player classification allows for considerable improvements on both game analytics and game adaptivity. With this paper we aim at reversing the ad-hoc tendency in player classifica-tion methods, by proposing an approach to player classifica-tion that can be integrated across different games and gen-r ..."
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Player classification allows for considerable improvements on both game analytics and game adaptivity. With this paper we aim at reversing the ad-hoc tendency in player classifica-tion methods, by proposing an approach to player classifica-tion that can be integrated across different games and gen-res and is particularly suited to be used by game designers. This paper describes our generic method of interaction-based player classification, which consists of three components: (i) intercepting player interactions, (ii) finding player types through fuzzy cluster analysis and (iii) classification using Hidden Markov Models (HMM). To showcase our method we developed Blindmaze, a simple web-based hidden maze game publicly available, featuring a bounded set of interac-tions. All data collected from a game is interaction-based, requiring minimal implementation effort from the game de-velopers. It is concluded that our method makes player clas-sification even more available by making it generic and re-usable across different games.
Classification of Online Game Players with ATPM and KLE Paper: jc11-3-2748: 2006/12/11 Classification of Online Game Players Using Action Transition Probability and Kullback Leibler Entropy
"... Online game players are more satisfied with contents tailored to their preferences. Player classification is necessary for determining which classes players be-long to. In this paper, we propose a new player clas-sification approach using action transition probability and Kullback Leibler entropy. I ..."
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Online game players are more satisfied with contents tailored to their preferences. Player classification is necessary for determining which classes players be-long to. In this paper, we propose a new player clas-sification approach using action transition probability and Kullback Leibler entropy. In experiments with two online game simulators, Zereal and Simac, our approach performed better than an existing approach based on action frequency and comparably to another existing approach using the Hidden Markov Model (HMM). Our approach takes into account both the fre-quency and order of player action. While HMM per-formance depends on its structure and initial parame-ters, our approach requires no parameter settings.
Structure Discovery in Hidden Markov Models
"... have not otherwise been submitted in any form for any degree or diploma to any tertiary institution. Where use has been made of the work of others it is duly acknowledged in the text. Name: Date: ..."
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have not otherwise been submitted in any form for any degree or diploma to any tertiary institution. Where use has been made of the work of others it is duly acknowledged in the text. Name: Date:
