Adaptive Game Environment Design based on Player Behavior Pattern for Difficulty Adjustment
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User churn in games often arises due to inadequate game difficulty. To address this, non-player characters (NPCs) has been utilized to modulate difficulty according to individual game skill. Nevertheless, the effectiveness of solely NPC-based adjustments is limited since game difficulty is influenced by both NPCs and environmental factors. This paper introduces a novel method for dynamically tailoring game difficulty by adjusting in-game environments based on player behavior patterns in top-down shooter game. Through analysis of diverse user game play data, we find that factors within the game environment, such as the distribution of enemy characters and the arrangement of terrain, have a substantial influence on the level of difficulty. Furthermore, it has been observed that behavioral patterns of players show variations according to changes in the game environment. Using these analytical result, we devise an artificial neural network model that configures an environment that suit player behavior patterns. With the model, we figure out the user player pattern and control the difficulty dynamically by changing the environment factors. Through the experiments, we show that our method provides an appropriate level of difficulty for users to prevent user churn.
Abstract
User churn in games often arises due to inadequate game difficulty. To address this, non-player characters (NPCs) has been utilized to modulate difficulty according to individual game skill. Nevertheless, the effectiveness of solely NPC-based adjustments is limited since game difficulty is influenced by both NPCs and environmental factors. This paper introduces a novel method for dynamically tailoring game difficulty by adjusting in-game environments based on player behavior patterns in top-down shooter game. Through analysis of diverse user game play data, we find that factors within the game environment, such as the distribution of enemy characters and the arrangement of terrain, have a substantial influence on the level of difficulty. Furthermore, it has been observed that behavioral patterns of players show variations according to changes in the game environment. Using these analytical result, we devise an artificial neural network model that configures an environment that suit player behavior patterns. With the model, we figure out the user player pattern and control the difficulty dynamically by changing the environment factors. Through the experiments, we show that our method provides an appropriate level of difficulty for users to prevent user churn.
참고문헌 (10)
- Oh, Inseok, "Creating pro-level AI for a real-time fighting game using deep reinforcement learning", IEEE Transactions on Games, 14(2) (2021)
- Hunicke, Robin, "AI for Dynamic Difficulty Adjustment in Games. 2004", Association for the Advancement of Artificial Intelligence (AAAI), pp.2~5 (2004)
- Zeng, Zheyin, "Dynamic Difficulty Adjustment in a Multiplayer Minecraft Server", Proceedings of the 34th Australian Conference on Human-Computer Interaction (2022)
- Silva, Mirna Paula, "Dynamic difficulty adjustment on MOBA games", Entertainment Computing, 18, pp.103~123 (2017)
- Zohaib, Mohammad, "Dynamic difficulty adjustment (DDA) in computer games: A review", Advances in Human-Computer Interaction (2018)
- Qin, Hua, "Effects of different scenarios of game difficulty on player immersion", Interacting with computers, 22(3), pp.230~239 (2010)
- Kim, JaeMin, "A study on the Game Difficulty Control based on Player Behavior Pattern Learning of AI", pp.2020
- Boger, Zvi, "Knowledge extraction from artificial neural network models", 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation (1997), IEEE
- Koster, Raph, "Theory of fun for game design" (2013), O'Reilly Media, Inc
- Csikszentmihalyi, Mihaly, "Optimal experience:Psychological studies of flow in consciousness" (1992), Cambridge university press