Proceedings

Towards Scalable MDP Algorithms

Download BibTeX Cite

Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 2818-2818.

https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-481

Abstract

The scalability of algorithms for solving Markov Decision Processes (MDPs) has been a limiting factor for MDPs as a modeling tool. This dissertation develops theoretical and empirical techniques for solving larger MDPs than was possible before, and aims to demonstrate the achieved progress by applying these new algorithms to a real-world problem.

Andrey Kolobov, Mausam, and Daniel S. Weld. 2011. Towards Scalable MDP Algorithms. In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11. IJCAI, 2818–2818. https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-481
Welcome Future Conferences AI Journal Division IJCAI Proceedings About IJCAI IJCAI Awards