Developing intelligent decision making systems in the real world requires planning algorithms which are able to deal with sources of uncertainty and constraints. An example can be found in smart distribution grids, in which planning can be used to decide when electric vehicles charge their batteries, such that the capacity limits of lines are respected at all times. In this particular example there can be uncertainty in the arrival time and charging demand of vehicles, and constraints follow directly from the capacity limits of the distribution grid to which vehicles are connected. Existing algorithms for planning under uncertainty subject to constraints are currently not suitable for these types of applications, and therefore this dissertation aims improve the applicability of these algorithms by advancing the state of the art in constrained multi-agent planning under uncertainty. The dissertation presents new algorithmic techniques for exact POMDP planning, finite-horizon POMDPs and POMDPs with constraints. Additionally, the dissertation shows how models for constrained planning can be used in smart distribution grids.
|Qualification||Doctor of Philosophy|
|Award date||27 May 2019|
|Publication status||Published - 27 May 2019|
- planning under uncertainty
- smart grids
- markov decision process
- partially observable markov decision process