Lower Bounding Klondike Solitaire with Monte-Carlo Planning

Despite its ubiquitous presence, very little is known about the odds of winning the simple card game of Klondike Solitaire. The main goal of this paper is to investigate the use of probabilistic planning to shed light on this issue. Unfortunatley, most probabilistic planning techniques are not well suited for Klondike due to the difficulties of representing the domain in standard planning languages and the complexity of the required search. Klondike thus serves as an interesting addition to the complement of probabilistic planning domains. In this paper, we study Klondike using several sampling-based planning approaches including UCT, hindsight optimization, and sparse sampling, and establish empirical lower bounds on their performance. We also introduce novel combinations of these approaches and evaluate them in Klondike. We provide a theoretical bound on the sample complexity of a method that naturally combines sparse sampling and UCT. Our results demonstrate that there is a policy ...

• Type: Conference
• Year: 2009
• Where: AIPS
• Authors: Ronald Bjarnason, Alan Fern, Prasad Tadepalli
• Keywords: AIPS 2009, Artificial Intelligence, Empirical Lower Bound, Klondike Solitaire, Probabilistic Planning