activity
20132026
most citedTowards Verified Artificial Intelligence

85 citations · 86 across the 5 of their papers we have counts for

collaborators

6 papers

math.OC2026

Moral Hazard in LTI Dynamics: A Hypothesis Testing Approach

Jaewon Jeong, Pan-Yang Su, S. Shankar Sastry +1

Many incentive design problems must contend with information asymmetries due to non-observation of efficiency (adverse selection) or non-observation of effort (moral hazard). And a…

cs.RO2025★ 1 cited

MRTA-Sim: A Modular Simulator for Multi-Robot Allocation, Planning, and Control in Open-World Environments

Victoria Marie Tuck, Hardik Parwana, Pei-Wei Chen +5

This paper introduces MRTA-Sim, a Python/ROS2/Gazebo simulator for testing approaches to Multi-Robot Task Allocation (MRTA) problems on simulated robots in complex, indoor environm…

cs.RO2024

SMT-Based Dynamic Multi-Robot Task Allocation

Victoria Marie Tuck, Pei-Wei Chen, Georgios Fainekos +4

Multi-Robot Task Allocation (MRTA) is a problem that arises in many application domains including package delivery, warehouse robotics, and healthcare. In this work, we consider th…

cs.AI2016★ 85 cited

Towards Verified Artificial Intelligence

Sanjit A. Seshia, Dorsa Sadigh, S. Shankar Sastry

Verified artificial intelligence (AI) is the goal of designing AI-based systems that that have strong, ideally provable, assurances of correctness with respect to mathematically-sp…

eess.SY2014

A Learning Based Approach to Control Synthesis of Markov Decision Processes for Linear Temporal Logic Specifications

Dorsa Sadigh, Eric S. Kim, Samuel Coogan +2

We propose to synthesize a control policy for a Markov decision process (MDP) such that the resulting traces of the MDP satisfy a linear temporal logic (LTL) property. We construct…

eess.SY2013

Robust Subspace System Identification via Weighted Nuclear Norm Optimization

Dorsa Sadigh, Henrik Ohlsson, S. Shankar Sastry +1

Subspace identification is a classical and very well studied problem in system identification. The problem was recently posed as a convex optimization problem via the nuclear norm…