Zobrazeno 1 - 10
of 444
pro vyhledávání: '"JANSEN, NILS"'
Markov decision processes (MDPs) are a standard model for sequential decision-making problems and are widely used across many scientific areas, including formal methods and artificial intelligence (AI). MDPs do, however, come with the restrictive ass
Externí odkaz:
http://arxiv.org/abs/2411.11451
Autor:
Galesloot, Maris F. L., Suilen, Marnix, Simão, Thiago D., Carr, Steven, Spaan, Matthijs T. J., Topcu, Ufuk, Jansen, Nils
Robust POMDPs extend classical POMDPs to handle model uncertainty. Specifically, robust POMDPs exhibit so-called uncertainty sets on the transition and observation models, effectively defining ranges of probabilities. Policies for robust POMDPs must
Externí odkaz:
http://arxiv.org/abs/2408.08770
Autor:
Jimenez-Roa, Lisandro A., Simão, Thiago D., Bukhsh, Zaharah, Tinga, Tiedo, Molegraaf, Hajo, Jansen, Nils, Stoelinga, Marielle
Publikováno v:
Proceedings of the 8th European Conference of The Prognostics and Health Management Society 2024
Large-scale infrastructure systems are crucial for societal welfare, and their effective management requires strategic forecasting and intervention methods that account for various complexities. Our study addresses two challenges within the Prognosti
Externí odkaz:
http://arxiv.org/abs/2407.12894
We consider the verification of neural network policies for reach-avoid control tasks in stochastic dynamical systems. We use a verification procedure that trains another neural network, which acts as a certificate proving that the policy satisfies t
Externí odkaz:
http://arxiv.org/abs/2406.00826
Autor:
Andriushchenko, Roman, Bork, Alexander, Budde, Carlos E., Češka, Milan, Grover, Kush, Hahn, Ernst Moritz, Hartmanns, Arnd, Israelsen, Bryant, Jansen, Nils, Jeppson, Joshua, Junges, Sebastian, Köhl, Maximilian A., Könighofer, Bettina, Křetínský, Jan, Meggendorfer, Tobias, Parker, David, Pranger, Stefan, Quatmann, Tim, Ruijters, Enno, Taylor, Landon, Volk, Matthias, Weininger, Maximilian, Zhang, Zhen
The analysis of formal models that include quantitative aspects such as timing or probabilistic choices is performed by quantitative verification tools. Broad and mature tool support is available for computing basic properties such as expected reward
Externí odkaz:
http://arxiv.org/abs/2405.13583
We present an A*-based algorithm to compute policies for finite-horizon Dec-POMDPs. Our goal is to sacrifice optimality in favor of scalability for larger horizons. The main ingredients of our approach are (1) using clustered sliding window memory, (
Externí odkaz:
http://arxiv.org/abs/2405.05662
Partially observable Markov decision processes (POMDPs) rely on the key assumption that probability distributions are precisely known. Robust POMDPs (RPOMDPs) alleviate this concern by defining imprecise probabilities, referred to as uncertainty sets
Externí odkaz:
http://arxiv.org/abs/2405.04941
Finite-state abstractions are widely studied for the automated synthesis of correct-by-construction controllers for stochastic dynamical systems. However, existing abstraction methods often lead to prohibitively large finite-state models. To address
Externí odkaz:
http://arxiv.org/abs/2404.01726
Labeled continuous-time Markov chains (CTMCs) describe processes subject to random timing and partial observability. In applications such as runtime monitoring, we must incorporate past observations. The timing of these observations matters but may b
Externí odkaz:
http://arxiv.org/abs/2401.06574