Sciweavers

Share
AIIDE
2008

Combining Model-Based Meta-Reasoning and Reinforcement Learning for Adapting Game-Playing Agents

10 years 5 months ago
Combining Model-Based Meta-Reasoning and Reinforcement Learning for Adapting Game-Playing Agents
Human experience with interactive games will be enhanced if the software agents that play the game learn from their failures. Techniques such as reinforcement learning provide one way in which these agents may learn from their failures. Model-based meta-reasoning, a technique in which an agent uses a self-model for blame assignment, provides another. This paper evaluates a framework in which both these approaches are combined. We describe an experimental investigation of a specific task (defending a city) in a computer war strategy game called FreeCiv. Our results indicate that in the task examined, model-based meta-reasoning coupled with reinforcement learning enables the agent to learn the task with performance matching that of an expert designed agent and with speed exceeding that of a pure reinforcement learning agent.
Patrick Ulam, Joshua Jones, Ashok K. Goel
Added 02 Oct 2010
Updated 02 Oct 2010
Type Conference
Year 2008
Where AIIDE
Authors Patrick Ulam, Joshua Jones, Ashok K. Goel
Comments (0)
books