Applying Agent Modeling to Behaviour Patterns of Characters in Story-Based Games

• Author / Creator

  Zhao, Richard

• Most story-based games today have manually-scripted non-player characters (NPCs) and the scripts are usually simple and repetitive since it is time-consuming for game developers to script each character individually. ScriptEase, a publicly-available author-oriented developer tool, attempts to solve this problem by generating script code from high-level design patterns, for BioWare Corp.'s role-playing game Neverwinter Nights. The ALeRT algorithm uses reinforcement learning (RL) to automatically generate NPC behaviours that change over time as the NPCs learn from the successes or failures of their own actions. This thesis aims to provide a new learning mechanism to game agents so they are capable of adapting to new behaviours based on the actions of other agents. The new on-line RL algorithm, ALeRT-AM, which includes an agent-modeling mechanism, is applied in a series of combat experiments in Neverwinter Nights and integrated into ScriptEase to produce adaptive behaviour patterns for NPCs.

• Subjects / Keywords

  • Scripting
  • Non-player character
  • ScriptEase
  • Variable exploration rate
  • Role-playing game
  • Behaviours
  • Reinforcement learning
  • ALeRT
  • Opponent modeling
  • Sarsa
  • Agent modeling
  • Variable learning rate

• Graduation date

  Fall 2009

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/R3KT3J

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.