Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
Defining personas in games using metrics
Future Play '08 Proceedings of the 2008 Conference on Future Play: Research, Play, Share
Player modeling using self-organization in tomb raider: underworld
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Dynamic self-organizing maps with controlled growth for knowledge discovery
IEEE Transactions on Neural Networks
Investigating individual decision making patterns in games using growing self organizing maps
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
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When interacting with computer games, a player would derive a strategy to conquer the game using his/her experience, observations etc. During game play, the players adapt their strategy to better suit the challenges posed by the game. With time, these strategies would formulate to a pattern of interaction for an individual player with respect to myriad of game entities such as on handling of artificial opponents, movement strategies and decision making. Understanding these patterns would provide valuable insight about a player's approach toward defeating the game which could be exploited to enhance the level of challenge posed by game AI. This paper attempts to identify dominant game play sequences made by an individual player by interpreting the positioning of the clusters in a growing self-organizing map (GSOM) generated using play data collected from the same player. Results indicate that dominant play sequences could indeed be identified but requires further analysis before a solid claim in this regard could be made.