28 citations · 73 across the 18 of their papers we have counts for
25 papers
Supermartingale Certificates for Parametric MDPs
Kaushik Mallik, Ðorđe Žikelić
We consider the problems of formal verification and synthesis in parametric Markov decision processes (MDPs) with general measurable state and action spaces. The heart of our appro…
Adaptive Strategies for GR(1) Games
S. Krishna, Shankaranarayanan Krishna, Kaushik Mallik +1
We consider two-player GR(1) games on graphs, where the system player Eve must satisfy \[ \Box\Diamond A_1\land\cdots\land\Box\Diamond A_m \;\implies\; \Box\Diamond G_1\land\cdots\…
Generalized Bidding Games: Where Bidding and Stochastic Games Meet
Ali Asadi, Thomas A. Henzinger, Ehsan Kafshdar Goharshady +2
Two-player games on graphs are a classical framework for analyzing strategic decision making. In turn-based games, two players move a token along the edges of the graph, and the ri…
Decoupled Planning for Multiple Omega-Regular Objectives
Guy Avni, Thomas A. Henzinger, Kaushik Mallik +2
We study the problem of generating paths on a graph that satisfy a collection of ω-regular objectives. We propose a decoupled framework in which each objective is assigned to an in…
Auction-Based Online Policy Adaptation for Evolving Objectives
Guruprerana Shabadi, Kaushik Mallik
We consider multi-objective reinforcement learning problems where objectives come from an identical family -- such as the class of reachability objectives -- and may appear or disa…
Monitoring of Static Fairness
Thomas A. Henzinger, Mahyar Karimi, Konstantin Kueffner +1
Machine-learned systems are in widespread use for making decisions about humans, and it is important that they are fair, i.e., not biased against individuals based on sensitive att…
Monitoring Robustness and Individual Fairness
Ashutosh Gupta, Thomas A. Henzinger, Konstantin Kueffner +2
Input-output robustness appears in various different forms in the literature, such as robustness of AI models to adversarial or semantic perturbations and individual fairness of AI…
Supermartingale Certificates for Quantitative Omega-regular Verification and Control
Thomas A. Henzinger, Kaushik Mallik, Pouya Sadeghi +1
We present the first supermartingale certificate for quantitative -regular properties of discrete-time infinite-state stochastic systems. Our certificate is defined on the produ…
Efficient Dynamic Shielding for Parametric Safety Specifications
Davide Corsi, Kaushik Mallik, Andoni Rodriguez +1
Shielding has emerged as a promising approach for ensuring safety of AI-controlled autonomous systems. The algorithmic goal is to compute a shield, which is a runtime safety enforc…
Bidding Games on Markov Decision Processes with Quantitative Reachability Objectives
Guy Avni, Martin Kurečka, Kaushik Mallik +2
Graph games are fundamental in strategic reasoning of multi-agent systems and their environments. We study a new family of graph games which combine stochastic environmental uncert…
Predictive Monitoring of Black-Box Dynamical Systems
Thomas A. Henzinger, Fabian Kresse, Kaushik Mallik +2
We study the problem of predictive runtime monitoring of black-box dynamical systems with quantitative safety properties. The black-box setting stipulates that the exact semantics…
Fairness Shields: Safeguarding against Biased Decision Makers
Filip Cano, Thomas A. Henzinger, Bettina Könighofer +2
As AI-based decision-makers increasingly influence human lives, it is a growing concern that their decisions are often unfair or biased with respect to people's sensitive attribute…
Bidding Games with Charging
Guy Avni, Ehsan Kafshdar Goharshady, Thomas A. Henzinger +1
Graph games lie at the algorithmic core of many automated design problems in computer science. These are games usually played between two players on a given graph, where the player…
Auction-Based Scheduling
Guy Avni, Kaushik Mallik, Suman Sadhukhan
Many sequential decision-making tasks require satisfaction of multiple, partially contradictory objectives. Existing approaches are monolithic, namely all objectives are fulfilled…
Monitoring Algorithmic Fairness under Partial Observations
Thomas A. Henzinger, Konstantin Kueffner, Kaushik Mallik
As AI and machine-learned software are used increasingly for making decisions that affect humans, it is imperative that they remain fair and unbiased in their decisions. To complem…
Monitoring Algorithmic Fairness
Thomas A. Henzinger, Mahyar Karimi, Konstantin Kueffner +1
Machine-learned systems are in widespread use for making decisions about humans, and it is important that they are fair, i.e., not biased against individuals based on sensitive att…
Runtime Monitoring of Dynamic Fairness Properties
Thomas A. Henzinger, Mahyar Karimi, Konstantin Kueffner +1
A machine-learned system that is fair in static decision-making tasks may have biased societal impacts in the long-run. This may happen when the system interacts with humans and fe…
Computing Adequately Permissive Assumptions for Synthesis
Ashwani Anand, Kaushik Mallik, Satya Prakash Nayak +1
We solve the problem of automatically computing a new class of environment assumptions in two-player turn-based finite graph games which characterize an ``adequate cooperation'' ne…
Symbolic Control for Stochastic Systems via Finite Parity Games
Rupak Majumdar, Kaushik Mallik, Anne-Kathrin Schmuck +1
We consider the problem of computing the maximal probability of satisfying an omega-regular specification for stochastic nonlinear systems evolving in discrete time. The problem re…
Resilient Abstraction-Based Controller Design
Stanly Samuel, Kaushik Mallik, Anne-Kathrin Schmuck +1
We consider the computation of resilient controllers for perturbed non-linear dynamical systems w.r.t. linear-time temporal logic specifications. We address this problem through th…
Symbolic Controller Synthesis for Büchi Specifications on Stochastic Systems
Rupak Majumdar, Kaushik Mallik, Sadegh Soudjani
We consider the policy synthesis problem for continuous-state controlled Markov processes evolving in discrete time, when the specification is given as a Büchi condition (visit a s…
Lazy Abstraction-Based Controller Synthesis
Kyle Hsu, Rupak Majumdar, Kaushik Mallik +1
We present lazy abstraction-based controller synthesis (ABCS) for continuous-time nonlinear dynamical systems against reach-avoid and safety specifications. State-of-the-art multi-…
Lazy Abstraction-Based Control for Safety Specifications
Kyle Hsu, Rupak Majumdar, Kaushik Mallik +1
We present a lazy version of multi-layered abstraction-based controller synthesis (ABCS) for continuous-time nonlinear dynamical systems against safety specifications. State-of-the…
Compositional Construction of Finite State Abstractions for Stochastic Control Systems
Kaushik Mallik, Sadegh Esmaeil Zadeh Soudjani, Anne-Kathrin Schmuck +1
Controller synthesis techniques for continuous systems with respect to temporal logic specifications typically use a finite-state symbolic abstraction of the system. Constructing t…