Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Ceska, Milan"'
Markov decision processes (MDPs) provide a fundamental model for sequential decision making under process uncertainty. A classical synthesis task is to compute for a given MDP a winning policy that achieves a desired specification. However, at design
Externí odkaz:
http://arxiv.org/abs/2407.12552
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
Probabilistic hyperproperties specify quantitative relations between the probabilities of reaching different target sets of states from different initial sets of states. This class of behavioral properties is suitable for capturing important security
Externí odkaz:
http://arxiv.org/abs/2307.04503
Autor:
Andriushchenko, Roman, Bork, Alexander, Češka, Milan, Junges, Sebastian, Katoen, Joost-Pieter, Macák, Filip
This paper marries two state-of-the-art controller synthesis methods for partially observable Markov decision processes (POMDPs), a prominent model in sequential decision making under uncertainty. A central issue is to find a POMDP controller - that
Externí odkaz:
http://arxiv.org/abs/2305.14149
Approximate circuits trading the power consumption for the quality of results play a key role in the development of energy-aware systems. Designing complex approximate circuits is, however, a very difficult and computationally demanding process. When
Externí odkaz:
http://arxiv.org/abs/2206.13077
Simulating chemical reaction networks is often computationally demanding, in particular due to stiffness. We propose a novel simulation scheme where long runs are not simulated as a whole but assembled from shorter precomputed segments of simulation
Externí odkaz:
http://arxiv.org/abs/2206.06677
We present a novel learning framework to obtain finite-state controllers (FSCs) for partially observable Markov decision processes and illustrate its applicability for indefinite-horizon specifications. Our framework builds on oracle-guided inductive
Externí odkaz:
http://arxiv.org/abs/2203.10803
This paper outlines two approaches|based on counterexample-guided abstraction refinement (CEGAR) and counterexample-guided inductive synthesis (CEGIS), respectively to the automated synthesis of finite-state probabilistic models and programs. Our CEG
Externí odkaz:
http://arxiv.org/abs/2105.13411
This paper presents a novel method for the automated synthesis of probabilistic programs. The starting point is a program sketch representing a finite family of finite-state Markov chains with related but distinct topologies, and a PCTL specification
Externí odkaz:
http://arxiv.org/abs/2101.12683
Publikováno v:
Applied Soft Computing, Volume 95, October 2020, 106466
We present a novel approach for designing complex approximate arithmetic circuits that trade correctness for power consumption and play important role in many energy-aware applications. Our approach integrates in a unique way formal methods providing
Externí odkaz:
http://arxiv.org/abs/2003.02491