Search Results for author: Krishnendu Chatterjee

Found 26 papers, 6 papers with code

Learning Stabilizing Policies in Stochastic Control Systems

no code implementations24 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.

Stability Verification in Stochastic Control Systems via Neural Network Supermartingales

no code implementations17 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.

Natural selection of mutants that modify population structure

no code implementations21 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.

Infinite Time Horizon Safety of Bayesian Neural Networks

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.

reinforcement-learning Safe Exploration

Evolutionary instability of selfish learning in repeated games

1 code implementation13 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.


On Satisficing in Quantitative Games

1 code implementation6 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.

Reinforcement Learning of Risk-Constrained Policies in Markov Decision Processes

1 code implementation27 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.

Decision Making Frame +1

Expectation Optimization with Probabilistic Guarantees in POMDPs with Discounted-sum Objectives

no code implementations27 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.

Decision Making Decision Making Under Uncertainty

Computational Approaches for Stochastic Shortest Path on Succinct MDPs

no code implementations24 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.

Algorithms and Conditional Lower Bounds for Planning Problems

no code implementations19 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.

Graph Planning with Expected Finite Horizon

no code implementations10 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 $T$.

Traveling Salesman Problem

Sensor Synthesis for POMDPs with Reachability Objectives

no code implementations29 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.

Faster Monte-Carlo Algorithms for Fixation Probability of the Moran Process on Undirected Graphs

no code implementations21 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.

Optimizing Expectation with Guarantees in POMDPs (Technical Report)

1 code implementation26 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).

Stochastic Shortest Path with Energy Constraints in POMDPs

no code implementations24 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.

A Symbolic SAT-based Algorithm for Almost-sure Reachability with Small Strategies in POMDPs

no code implementations26 Nov 2015 Krishnendu Chatterjee, Martin Chmelik, Jessica Davies

POMDPs are standard models for probabilistic planning problems, where an agent interacts with an uncertain environment.

Algorithmic Analysis of Qualitative and Quantitative Termination Problems for Affine Probabilistic Programs

no code implementations28 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

Algorithms for Algebraic Path Properties in Concurrent Systems of Constant Treewidth Components

no code implementations26 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

MultiGain: A controller synthesis tool for MDPs with multiple mean-payoff objectives

no code implementations13 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.

Automatic Generation of Alternative Starting Positions for Simple Traditional Board Games

no code implementations14 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.

Board Games

Optimal Cost Almost-sure Reachability in POMDPs

no code implementations14 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.

A Framework for Automated Competitive Analysis of On-line Scheduling of Firm-Deadline Tasks

no code implementations8 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

POMDPs under Probabilistic Semantics

no code implementations9 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.

Generalized Risk-Aversion in Stochastic Multi-Armed Bandits

no code implementations5 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.

Multi-Armed Bandits

POMDPs under Probabilistic Semantics

no code implementations22 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.

Linear Time Algorithm for Weak Parity Games

1 code implementation9 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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