Proceedings

Execution Monitoring as Meta-Games for General Game-Playing Robots

David Rajaratnam, Michael Thielscher

Download BibTeX Cite

Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Special Track on Knowledge Representation and Reasoning. Pages 3178-3184.

Abstract

General Game Playing aims to create AI systems that can understand the rules of new games and learn to play them effectively without human intervention. The recent proposal for general game-playing robots extends this to AI systems that play games in the real world. Execution monitoring becomes a necessity when moving from a virtual to a physical environment, because in reality actions may not be executed properly and (human) opponents may make illegal game moves. We develop a formal framework for execution monitoring by which an action theory that provides an axiomatic description of a game is automatically embedded in a meta-game for a robotic player — called the arbiter — whose role is to monitor and correct failed actions. This allows for the seamless encoding of recovery behaviours within a meta-game, enabling a robot to recover from these unexpected events.

David Rajaratnam, and Michael Thielscher. 2015. Execution Monitoring as Meta-Games for General Game-Playing Robots. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15. IJCAI, 3178–3184. https://ijcai.org/proceedings/2015/448
Welcome Future Conferences AI Journal Division IJCAI Proceedings About IJCAI IJCAI Awards