51 citations · 118 across the 22 of their papers we have counts for
6 papers · 1 filter
Towards a Systematic Computational Framework for Modeling Multi-Agent Decision-Making at Micro Level for Smart Vehicles in a Smart World
Qi Dai, Xunnong Xu, Wen Guo +2
We propose a multi-agent based computational framework for modeling decision-making and strategic interaction at micro level for smart vehicles in a smart world. The concepts of Ma…
Towards Interpretable-AI Policies Induction using Evolutionary Nonlinear Decision Trees for Discrete Action Systems
Yashesh Dhebar, Kalyanmoy Deb, Subramanya Nageshrao +2
Black-box AI induction methods such as deep reinforcement learning (DRL) are increasingly being used to find optimal policies for a given control task. Although policies represente…
An online evolving framework for advancing reinforcement-learning based automated vehicle control
Teawon Han, Subramanya Nageshrao, Dimitar P. Filev +1
In this paper, an online evolving framework is proposed to detect and revise a controller's imperfect decision-making in advance. The framework consists of three modules: the evolv…
Action Governor for Discrete-Time Linear Systems with Non-Convex Constraints
Nan Li, Kyoungseok Han, Anouck Girard +3
This paper introduces an add-on, supervisory scheme, referred to as Action Governor (AG), for discrete-time linear systems to enforce exclusion-zone avoidance requirements. It does…
A Game Theoretic Approach for Parking Spot Search with Limited Parking Lot Information
Yutong Li, Nan Li, H. Eric Tseng +4
We propose a game theoretic approach to address the problem of searching for available parking spots in a parking lot and picking the ``optimal'' one to park. The approach exploits…
Vision-Based Autonomous Driving: A Model Learning Approach
Ali Baheri, Ilya Kolmanovsky, Anouck Girard +2
We present an integrated approach for perception and control for an autonomous vehicle and demonstrate this approach in a high-fidelity urban driving simulator. Our approach first…