The recent proliferation of smart-phones and other wearable devices has lead
to a surge of new mobile applications. Partially observable Markov decision
processes provide a natural framework to design applications that
continuously make decisions based on noisy sensor measurements. However,
given the limited battery life, there is a need to minimize the amount of
online computation. This can be achieved by compiling a policy into a
finite state controller since there is no need for belief monitoring or
online search. In this paper, we propose a new branch and bound technique
to search for a good controller. In contrast to many existing algorithms
for controllers, our search technique is not subject to local optima. We
also show how to reduce the amount of search by avoiding the enumeration of
isomorphic controllers and by taking advantage of suitable upper and lower
bounds. The approach is demonstrated on several benchmark problems as well
as a smart-phone application to assist persons with Alzheimer's to wayfind
