no code implementations • 22 Jan 2024 • Raphaël Berthon, Joost-Pieter Katoen, Munyque Mittelmann, Aniello Murano
We also give a 2NEXPTIME complexity result for NatPATL* with the same restriction.
no code implementations • 17 May 2023 • Bahare Salmani, Joost-Pieter Katoen
This paper addresses the $\epsilon$-close parameter tuning problem for Bayesian Networks (BNs): find a minimal $\epsilon$-close amendment of probability entries in a given set of (rows in) conditional probability tables that make a given quantitative constraint on the BN valid.
no code implementations • 15 Feb 2022 • Kevin Batz, Adrian Gallus, Benjamin Lucien Kaminski, Joost-Pieter Katoen, Tobias Winkler
We study weighted programming, a programming paradigm for specifying mathematical models.
no code implementations • 21 Jan 2022 • Alexander Bork, Joost-Pieter Katoen, Tim Quatmann
We consider the problem: is the optimal expected total reward to reach a goal state in a partially observable Markov decision process (POMDP) below a given threshold?
1 code implementation • 30 Jun 2021 • Murat Cubuktepe, Nils Jansen, Sebastian Junges, Joost-Pieter Katoen, Ufuk Topcu
The parameter synthesis problem is to compute an instantiation of these unspecified parameters such that the resulting MDP satisfies the temporal logic specification.
no code implementations • 29 May 2021 • Bahare Salmani, Joost-Pieter Katoen
This paper proposes various new analysis techniques for Bayes networks in which conditional probability tables (CPTs) may contain symbolic variables.
no code implementations • 29 Jan 2021 • Roman Andriushchenko, Milan Ceska, Sebastian Junges, Joost-Pieter Katoen
The method builds on a novel inductive oracle that greedily generates counter-examples (CEs) for violating programs and uses them to prune the family.
no code implementations • 28 Jan 2021 • Martin Grohe, Benjamin Lucien Kaminski, Joost-Pieter Katoen, Peter Lindner
In (Grohe, Kaminski, Katoen, Lindner; PODS 2020) we extend the declarative probabilistic programming language Generative Datalog, proposed by (B\'ar\'any et al.~2017) for discrete probability distributions, to continuous probability distributions and show that such programs yield generative models of continuous probabilistic databases.
Probabilistic Programming Databases
no code implementations • 29 Jul 2020 • Bahare Salmani, Joost-Pieter Katoen
This paper applies probabilistic model checking techniques for discrete Markov chains to inference in Bayesian networks.
1 code implementation • 30 Jun 2020 • Alexander Bork, Sebastian Junges, Joost-Pieter Katoen, Tim Quatmann
This paper considers the verification problem for partially observable MDPs, in which the policies make their decisions based on (the history of) the observations emitted by the system.
no code implementations • 24 Oct 2019 • Florent Delgrange, Joost-Pieter Katoen, Tim Quatmann, Mickael Randour
That is, strategies that are pure (no randomization) and have bounded memory.
1 code implementation • 28 Apr 2019 • Milan Češka, Christian Hensel, Sebastian Junges, Joost-Pieter Katoen
Probabilistic programs are key to deal with uncertainty in e. g. controller synthesis.
1 code implementation • 15 Feb 2019 • Milan Ceska, Nils Jansen, Sebastian Junges, Joost-Pieter Katoen
This paper considers large families of Markov chains (MCs) that are defined over a set of parameters with finite discrete domains.
no code implementations • 28 Sep 2018 • Mohamadreza Ahmadi, Murat Cubuktepe, Nils Jansen, Sebastian Junges, Joost-Pieter Katoen, Ufuk Topcu
Then, the deception problem is to compute a strategy for the deceiver that minimizes the expected cost of deception against all strategies of the infiltrator.
no code implementations • 5 Mar 2018 • Murat Cubuktepe, Nils Jansen, Sebastian Junges, Joost-Pieter Katoen, Ufuk Topcu
This paper considers parametric Markov decision processes (pMDPs) whose transitions are equipped with affine functions over a finite set of parameters.
no code implementations • 14 Aug 2017 • Leonore Winterer, Sebastian Junges, Ralf Wimmer, Nils Jansen, Ufuk Topcu, Joost-Pieter Katoen, Bernd Becker
We study synthesis problems with constraints in partially observable Markov decision processes (POMDPs), where the objective is to compute a strategy for an agent that is guaranteed to satisfy certain safety and performance specifications.
1 code implementation • 28 Oct 2016 • Sebastian Junges, Nils Jansen, Joost-Pieter Katoen, Ufuk Topcu
Probabilistic model checking is used to predict the human's behavior.