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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

We find that selfish learning is insufficient to explain human behavior when there is a trade-off between payoff maximization and fairness.

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 • 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 • 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 • 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

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