Online Algorithms Modeled After Mousehunt

January 08, 2015 Β· Declared Dead Β· πŸ› arXiv.org

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Authors Jeffrey Ling, Kai Xiao, Dai Yang arXiv ID 1501.01720 Category cs.DS: Data Structures & Algorithms Citations 0 Venue arXiv.org Last Checked 5 months ago
Abstract
In this paper we study a variety of novel online algorithm problems inspired by the game Mousehunt. We consider a number of basic models that approximate the game, and we provide solutions to these models using Markov Decision Processes, deterministic online algorithms, and randomized online algorithms. We analyze these solutions' performance by deriving results on their competitive ratios.
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