Name
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. |
Year | Venue | Field |
|---|---|---|
2015 | CoRR | Online algorithm,Computer science,Markov decision process,Theoretical computer science,Randomized algorithms as zero-sum games |
DocType | Volume | Citations |
Journal | abs/1501.01720 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeffrey Ling | 1 | 7 | 0.84 |
| Kai Xiao | 2 | 0 | 2.37 |
| Dai Yang | 3 | 0 | 1.35 |