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20182026
most citedCompositional Reinforcement Learning from Logical Specifications

10 citations · 16 across the 18 of their papers we have counts for

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6 papers · 1 filter

cs.FL2023★ 2 cited

Model Checking Strategies from Synthesis Over Finite Traces

Suguman Bansal, Yong Li, Lucas Martinelli Tabajara +2

The innovations in reactive synthesis from {\em Linear Temporal Logics over finite traces} (LTLf) will be amplified by the ability to verify the correctness of the strategies gener…

cs.FL2021

A Framework for Transforming Specifications in Reinforcement Learning

Rajeev Alur, Suguman Bansal, Osbert Bastani +1

Reactive synthesis algorithms allow automatic construction of policies to control an environment modeled as a Markov Decision Process (MDP) that are optimal with respect to high-le…

cs.FL2021★ 1 cited

Adapting Behaviors via Reactive Synthesis

Gal Amram, Suguman Bansal, Dror Fried +3

In the \emph{Adapter Design Pattern}, a programmer implements a \emph{Target} interface by constructing an \emph{Adapter} that accesses an existing \emph{Adaptee} code. In this wor…

cs.FL2021

On Satisficing in Quantitative Games

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. {\em Optimization} is one form of analysis. We argue that…

cs.FL2020

Automata-based Quantitative Verification

Suguman Bansal

Quantitative analysis of computing systems is an emerging area in automated formal analysis. Such properties address aspects such as costs and rewards, quality measures, resource c…

cs.FL2018★ 1 cited

Comparator automata in quantitative verification

Suguman Bansal, Swarat Chaudhuri, Moshe Y. Vardi

The notion of comparison between system runs is fundamental in formal verification. This concept is implicitly present in the verification of qualitative systems, and is more prono…