34 citations · 50 across the 6 of their papers we have counts for
6 papers
The Measurement Imbalance in Agentic AI Evaluation Undermines Industry Productivity Claims
Kiana Jafari Meimandi, Gabriela Aránguiz-Dias, Grace Ra Kim +3
As industry reports claim agentic AI systems deliver double-digit productivity gains and multi-trillion dollar economic potential, the validity of these claims has become critical…
Open Problems in Technical AI Governance
Anka Reuel, Ben Bucknall, Stephen Casper +30
AI progress is creating a growing range of risks and opportunities, but it is often unclear how they should be navigated. In many cases, the barriers and uncertainties faced are at…
Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation
Jiachen Li, Chuanbo Hua, Jianpeng Yao +4
Social robot navigation can be helpful in various contexts of daily life but requires safe human-robot interactions and efficient trajectory planning. While modeling pairwise relat…
Robust Driving Policy Learning with Guided Meta Reinforcement Learning
Kanghoon Lee, Jiachen Li, David Isele +3
Although deep reinforcement learning (DRL) has shown promising results for autonomous navigation in interactive traffic scenarios, existing work typically adopts a fixed behavior p…
Health-Informed Policy Gradients for Multi-Agent Reinforcement Learning
Ross E. Allen, Jayesh K. Gupta, Jaime Pena +3
This paper proposes a definition of system health in the context of multiple agents optimizing a joint reward function. We use this definition as a credit assignment term in a poli…
Algorithms for Verifying Deep Neural Networks
Changliu Liu, Tomer Arnon, Christopher Lazarus +3
Deep neural networks are widely used for nonlinear function approximation with applications ranging from computer vision to control. Although these networks involve the composition…