no code implementations • 18 Dec 2024 • Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Mehrdad Karrabi, Harshit J Motwani, Maximilian Seeliger, Đorđe Žikelić
While there has been much work on checking satisfiability of unquantified LRA and NRA formulas, the problem of checking satisfiability of quantified LRA and NRA formulas remains a significant challenge.
no code implementations • 16 Dec 2024 • Krishnendu Chatterjee, Ruichen Luo, Raimundo Saona, Jakub Svoboda
This class of optimization problems has been studied under restrictive conditions, such as, (C1) the halting or stability condition; (C2) the non-negative coefficients condition; (C3) the sum up to 1 condition; and (C4) the only min or only max oerator condition.
no code implementations • 7 May 2024 • S. Akshay, Krishnendu Chatterjee, Tobias Meggendorfer, Đorđe Žikelić
In this work, we consider the problems of certified policy (i. e. controller) verification and synthesis in MDPs under distributional reach-avoidance specifications.
no code implementations • 14 Mar 2024 • Tomáš Brázdil, Krishnendu Chatterjee, Martin Chmelik, Vojtěch Forejt, Jan Křetínský, Marta Kwiatkowska, Tobias Meggendorfer, David Parker, Mateusz Ujma
The presented framework focuses on probabilistic reachability, which is a core problem in verification, and is instantiated in two distinct scenarios.
no code implementations • 26 Jan 2024 • Jakub Svoboda, Soham Joshi, Josef Tkadlec, Krishnendu Chatterjee
Amplifiers of selection are networks that increase the fixation probability of advantageous mutants, as compared to the unstructured fully-connected network.
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.
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).
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 • 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.
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.
no code implementations • 21 Nov 2021 • Josef Tkadlec, Kamran Kaveh, Krishnendu Chatterjee, Martin A. Nowak
Finally, we show that for low-dimensional lattices, the effect of altered motility is comparable to that of altered fitness: in the limit of large population size, the invader's fixation probability is either constant or exponentially small, depending on whether it is more or less motile than the resident.
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 • 13 May 2021 • Alex McAvoy, Julian Kates-Harbeck, Krishnendu Chatterjee, Christian Hilbe
However, when two such "selfish" learners interact with each other, the outcome can be detrimental to both, especially when there are conflicts of interest.
1 code implementation • 6 Jan 2021 • Suguman Bansal, Krishnendu Chatterjee, Moshe Y. Vardi
Several problems in planning and reactive synthesis can be reduced to the analysis of two-player quantitative graph games.
1 code implementation • 27 Feb 2020 • Tomas Brazdil, Krishnendu Chatterjee, Petr Novotny, Jiri Vahala
The objective of risk-constrainedplanning is to maximize the expected discounted-sum payoffamong risk-averse policies that ensure the probability to en-counter a failure state is below a desired threshold.
no code implementations • 27 Apr 2018 • Krishnendu Chatterjee, Adrián Elgyütt, Petr Novotný, Owen Rouillé
We consider the expectation optimization with probabilistic guarantee (EOPG) problem, where the goal is to optimize the expectation ensuring that the payoff is above a given threshold with at least a specified probability.
no code implementations • 24 Apr 2018 • Krishnendu Chatterjee, Hongfei Fu, Amir Kafshdar Goharshady, Nastaran Okati
We consider the stochastic shortest path (SSP) problem for succinct Markov decision processes (MDPs), where the MDP consists of a set of variables, and a set of nondeterministic rules that update the variables.
no code implementations • 19 Apr 2018 • Krishnendu Chatterjee, Wolfgang Dvořák, Monika Henzinger, Alexander Svozil
For the sequential target problem, we present a linear-time algorithm for graphs, a sub-quadratic algorithm for MDPs, and a quadratic conditional lower bound for games on graphs.
no code implementations • 10 Feb 2018 • Krishnendu Chatterjee, Laurent Doyen
A classical problem in discrete planning is to consider a weighted graph and construct a path that maximizes the sum of weights for a given time horizon $T$.
no code implementations • 29 Sep 2017 • Krishnendu Chatterjee, Martin Chmelik, Ufuk Topcu
Partially observable Markov decision processes (POMDPs) are widely used in probabilistic planning problems in which an agent interacts with an environment using noisy and imprecise sensors.
no code implementations • 21 Jun 2017 • Krishnendu Chatterjee, Rasmus Ibsen-Jensen, Martin A. Nowak
We present faster polynomial-time Monte-Carlo algorithms for finidng the fixation probability on undirected graphs.
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).
no code implementations • 24 Feb 2016 • Tomáš Brázdil, Krishnendu Chatterjee, Martin Chmelík, Anchit Gupta, Petr Novotný
Finally, we show experimentally that our algorithm performs well and computes succinct policies on a number of POMDP instances from the literature that were naturally enhanced with energy levels.
no code implementations • 26 Nov 2015 • Krishnendu Chatterjee, Martin Chmelik, Jessica Davies
POMDPs are standard models for probabilistic planning problems, where an agent interacts with an uncertain environment.
no code implementations • 28 Oct 2015 • Krishnendu Chatterjee, Hongfei Fu, Petr Novotny, Rouzbeh Hasheminezhad
Firstly, we show that the membership problem of LRAPP (i) can be decided in polynomial time for APP's with at most demonic non-determinism, and (ii) is NP-hard and in PSPACE for APP's with angelic non-determinism; moreover, the NP-hardness result holds already for APP's without probability and demonic non-determinism.
Logic in Computer Science Programming Languages D.2.4
no code implementations • 26 Oct 2015 • Krishnendu Chatterjee, Amir Kafshdar Goharshady, Rasmus Ibsen-Jensen, Andreas Pavlogiannis
For example, in a concurrent system of two components, the traditional approach requires hexic time in the worst case for answering one query as well as computing the transitive closure, whereas we show that with one-time preprocessing in almost cubic time, each subsequent query can be answered in at most linear time, and even the transitive closure can be computed in almost quartic time.
Programming Languages Data Structures and Algorithms F.3.2
no code implementations • 13 Jan 2015 • Tomáš Brázdil, Krishnendu Chatterjee, Vojtěch Forejt, Antonín Kučera
We present MultiGain, a tool to synthesize strategies for Markov decision processes (MDPs) with multiple mean-payoff objectives.
no code implementations • 14 Nov 2014 • Krishnendu Chatterjee, Martin Chmelík, Raghav Gupta, Ayush Kanodia
We consider partially observable Markov decision processes (POMDPs) with a set of target states and every transition is associated with an integer cost.
no code implementations • 14 Nov 2014 • Umair Z. Ahmed, Krishnendu Chatterjee, Sumit Gulwani
Simple board games, like Tic-Tac-Toe and CONNECT-4, play an important role not only in the development of mathematical and logical skills, but also in the emotional and social development.
no code implementations • 8 Sep 2014 • Krishnendu Chatterjee, Andreas Pavlogiannis, Alexander Kößler, Ulrich Schmid
We present a flexible framework for the automated competitive analysis of on-line scheduling algorithms for firm-deadline real-time tasks based on multi-objective graphs: Given a taskset and an on-line scheduling algorithm specified as a labeled transition system, along with some optional safety, liveness, and/or limit-average constraints for the adversary, we automatically compute the competitive ratio of the algorithm w. r. t.
Data Structures and Algorithms Systems and Control
no code implementations • 9 Aug 2014 • Krishnendu Chatterjee, Martin Chmelik
We consider two types of path constraints: (i) quantitative constraint defines the set of paths where the payoff is at least a given threshold lambda_1 in (0, 1]; and (ii) qualitative constraint which is a special case of quantitative constraint with lambda_1=1.
no code implementations • 5 May 2014 • Alexander Zimin, Rasmus Ibsen-Jensen, Krishnendu Chatterjee
We consider the problem of minimizing the regret in stochastic multi-armed bandit, when the measure of goodness of an arm is not the mean return, but some general function of the mean and the variance. We characterize the conditions under which learning is possible and present examples for which no natural algorithm can achieve sublinear regret.
no code implementations • 22 Aug 2013 • Krishnendu Chatterjee, Martin Chmelík
We consider two types of path constraints: (i) quantitative constraint defines the set of paths where the payoff is at least a given threshold {\lambda} in (0, 1]; and (ii) qualitative constraint which is a special case of quantitative constraint with {\lambda} = 1.
1 code implementation • 9 May 2008 • Krishnendu Chatterjee
We consider games played on graphs with the winning conditions for the players specified as weak-parity conditions.
Logic in Computer Science