  This paper considers the complex problem of a team of UAVs searching targets
under uncertainty. The goal of the UAV team is to find all of the moving
targets as quickly as possible before they arrive at their selected goal. The
uncertainty considered is threefold: First, the UAVs do not know the targets'
locations and destinations. Second, the sensing capabilities of the UAVs are
not perfect. Third, the targets' movement model is unknown. We suggest a
real-time algorithmic framework for the UAVs, combining entropy and
stochastic-temporal belief, that aims at optimizing the probability of a quick
and successful detection of all of the targets. We have empirically evaluated
the algorithmic framework, and have shown its efficiency and significant
performance improvement compared to other solutions. Furthermore, we have
evaluated our framework using Peer Designed Agents (PDAs), which are computer
agents that simulate targets and show that our algorithmic framework
outperforms other solutions in this scenario.
