1 code implementation • 21 Dec 2023 • Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Mehrdad Karrabi, Petr Novotný, Đorđe Žikelić
We present a novel perspective on this problem and show that it can be reduced to solving long-run average reward turn-based stochastic games with finite state and action spaces.
1 code implementation • NeurIPS 2023 • Đorđe Žikelić, Mathias Lechner, Abhinav Verma, Krishnendu Chatterjee, Thomas A. Henzinger
We also derive a tighter lower bound compared to previous work on the probability of reach-avoidance implied by a RASM, which is required to find a compositional policy with an acceptable probabilistic threshold for complex tasks with multiple edge policies.
no code implementations • 27 Jul 2023 • Guy Avni, Tobias Meggendorfer, Suman Sadhukhan, Josef Tkadlec, Đorđe Žikelić
We consider, for the first time, {\em poorman discrete-bidding} in which the granularity of the bids is restricted and the higher bid is paid to the bank.
1 code implementation • 29 Nov 2022 • Mathias Lechner, Đorđe Žikelić, Krishnendu Chatterjee, Thomas A. Henzinger, Daniela Rus
We study the problem of training and certifying adversarially robust quantized neural networks (QNNs).
no code implementations • 11 Oct 2022 • Đorđe Žikelić, Mathias Lechner, Thomas A. Henzinger, Krishnendu Chatterjee
We study the problem of learning controllers for discrete-time non-linear stochastic dynamical systems with formal reach-avoid guarantees.
1 code implementation • 11 Oct 2022 • Matin Ansaripour, Krishnendu Chatterjee, Thomas A. Henzinger, Mathias Lechner, Đorđe Žikelić
We show that this procedure can also be adapted to formally verifying that, under a given Lipschitz continuous control policy, the stochastic system stabilizes within some stabilizing region with probability~$1$.
no code implementations • 24 May 2022 • Đorđe Žikelić, Mathias Lechner, Krishnendu Chatterjee, Thomas A. Henzinger
In this work, we address the problem of learning provably stable neural network policies for stochastic control systems.
no code implementations • 17 Dec 2021 • Mathias Lechner, Đorđe Žikelić, Krishnendu Chatterjee, Thomas A. Henzinger
We consider the problem of formally verifying almost-sure (a. s.) asymptotic stability in discrete-time nonlinear stochastic control systems.
1 code implementation • NeurIPS 2021 • Mathias Lechner, Đorđe Žikelić, Krishnendu Chatterjee, Thomas A. Henzinger
Bayesian neural networks (BNNs) place distributions over the weights of a neural network to model uncertainty in the data and the network's prediction.
1 code implementation • 15 Dec 2020 • Thomas A. Henzinger, Mathias Lechner, Đorđe Žikelić
In this paper, we show that verifying the bit-exact implementation of quantized neural networks with bit-vector specifications is PSPACE-hard, even though verifying idealized real-valued networks and satisfiability of bit-vector specifications alone are each in NP.
no code implementations • 12 May 2020 • Guy Avni, Ismaël Jecker, Đorđe Žikelić
In {\em bidding games}, however, the players have budgets, and in each turn, we hold an "auction" (bidding) to determine which player moves the token: both players simultaneously submit bids and the higher bidder moves the token.
1 code implementation • 26 Nov 2016 • Krishnendu Chatterjee, Petr Novotný, Guillermo A. Pérez, Jean-François Raskin, Đorđe Žikelić
In this work we go beyond both the "expectation" and "threshold" approaches and consider a "guaranteed payoff optimization (GPO)" problem for POMDPs, where we are given a threshold $t$ and the objective is to find a policy $\sigma$ such that a) each possible outcome of $\sigma$ yields a discounted-sum payoff of at least $t$, and b) the expected discounted-sum payoff of $\sigma$ is optimal (or near-optimal) among all policies satisfying a).