Online Algorithms Modeled After Mousehunt
Jeffrey Ling, Kai Xiao, Dai Yang
TL;DR
This paper explores online algorithms inspired by Mousehunt, using Markov Decision Processes and other methods to analyze their performance through competitive ratios.
Contribution
It introduces novel online algorithm models based on Mousehunt and provides performance analysis using various algorithmic strategies.
Findings
Derived competitive ratios for proposed algorithms
Compared deterministic and randomized approaches
Provided insights into online decision-making performance
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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Taxonomy
TopicsOptimization and Search Problems · Auction Theory and Applications · Advanced Bandit Algorithms Research