Partially observable Markov decision processes (POMDPs) provide an elegant mathematical framework for modeling complex decision and planning problems in stochastic domains in whic...
We study the computational complexity of some central analysis problems for One-Counter Markov Decision Processes (OC-MDPs), a class of finitely-presented, countable-state MDPs. O...
Tomas Brazdil, Vaclav Brozek, Kousha Etessami, Ant...
We present uniform approaches to establish complexity bounds for decision problems such as reachability and simulation, that arise naturally in the verification of timed software s...
Rohit Chadha, Axel Legay, Pavithra Prabhakar, Mahe...
Recent research has recognised that multicriteria decision making (MCDM) should take account of uncertainty, risk and confidence. This paper takes this research forward by using l...
Decentralized partially observable Markov decision process (DEC-POMDP) is an approach to model multi-robot decision making problems under uncertainty. Since it is NEXP-complete the...