  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.
