This paper models and analyzes serial production lines with specialists at each station and a single, cross-trained floating worker who can work at any station. We formulate Marko...
Linn I. Sennott, Mark P. Van Oyen, Seyed M. R. Ira...
We address the problem of optimally controlling stochastic environments that are partially observable. The standard method for tackling such problems is to define and solve a Part...
Partially Observable Markov Decision Processes (POMDPs) provide a rich framework for sequential decision-making under uncertainty in stochastic domains. However, solving a POMDP i...
We consider reinforcement learning in the parameterized setup, where the model is known to belong to a parameterized family of Markov Decision Processes (MDPs). We further impose ...
We consider the problem of controlling a continuous-time linear stochastic system from a specification given as a Linear Temporal Logic (LTL) formula over a set of linear predicate...
Morteza Lahijanian, Sean B. Andersson, Calin Belta