
ScenarioBased Verification of Uncertain MDPs
We consider Markov decision processes (MDPs) in which the transition pro...
read it

Stochastic Finite State Control of POMDPs with LTL Specifications
Partially observable Markov decision processes (POMDPs) provide a modeli...
read it

Robust Combination of Local Controllers
Planning problems are hard, motion planning, for example, isPSPACEhard....
read it

RiskAverse Planning Under Uncertainty
We consider the problem of designing policies for partially observable M...
read it

Optimal Inspection and Maintenance Planning for Deteriorating Structures through Dynamic Bayesian Networks and Markov Decision Processes
Civil and maritime engineering systems, among others, from bridges to of...
read it

Learning FiniteState Controllers for Partially Observable Environments
Reactive (memoryless) policies are sufficient in completely observable M...
read it

Strengthening Deterministic Policies for POMDPs
The synthesis problem for partially observable Markov decision processes...
read it
Robust FiniteState Controllers for Uncertain POMDPs
Uncertain partially observable Markov decision processes (uPOMDPs) allow the probabilistic transition and observation functions of standard POMDPs to belong to a socalled uncertainty set. Such uncertainty sets capture uncountable sets of probability distributions. We develop an algorithm to compute finitememory policies for uPOMDPs that robustly satisfy given specifications against any admissible distribution. In general, computing such policies is both theoretically and practically intractable. We provide an efficient solution to this problem in four steps. (1) We state the underlying problem as a nonconvex optimization problem with infinitely many constraints. (2) A dedicated dualization scheme yields a dual problem that is still nonconvex but has finitely many constraints. (3) We linearize this dual problem and (4) solve the resulting finite linear program to obtain locally optimal solutions to the original problem. The resulting problem formulation is exponentially smaller than those resulting from existing methods. We demonstrate the applicability of our algorithm using large instances of an aircraft collisionavoidance scenario and a novel spacecraft motion planning case study.
READ FULL TEXT
Comments
There are no comments yet.