The LSD tree: spatial access to multidimensional and non-point objects
VLDB '89 Proceedings of the 15th international conference on Very large data bases
Case-based reasoning
A hybrid split strategy for k-d-tree based access structures G⃗
GIS '96 Proceedings of the 4th ACM international workshop on Advances in geographic information systems
Data Structures for Range Searching
ACM Computing Surveys (CSUR)
It knows what you're going to do: adding anticipation to a Quakebot
Proceedings of the fifth international conference on Autonomous agents
GameBots: a flexible test bed for multiagent team research
Communications of the ACM - Internet abuse in the workplace and Game engines in scientific research
Machine Learning
A semantic generation framework for enabling adaptive game worlds
Proceedings of the 8th International Conference on Advances in Computer Entertainment Technology
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Nonplayer characters (NPCs) in today's computer games lack the ability to adapt to situations that were not envisaged by the artificial intelligence (AI) programmer. This lack of adaptation produces lifeless characters that are prone to repetitive and predictable behavior. In this article, we present our work towards the development of an online learning and adaptation architecture for NPCs in first-person shooter (FPS) computer games. Our architecture builds upon incremental case-based approaches to modelling an observed entity, and makes a number of novel contributions. In particular, we develop a dual state representation to enhance case matching, and use adaptive k-d tree-based techniques to improve case storage and retrieval. The dual state representation allows more game features to be represented in the system, which enables observed behavior to be more accurately recorded and actions predicted. The system is applied to the Unreal Tournament using the GameBots API and evaluated in a number of different game scenarios. Our results show that the adaptation system can accurately predict a human player's actions and that our dual state representation enhances prediction. We also demonstrate that an adaptive k-d tree-based technique can be used online to maintain a balanced tree of observed cases.