Game-theoretic inverse learning is the problem of inferring the players'...
In interactive multi-agent settings, decision-making complexity arises f...
In this work, we develop a scalable, local trajectory optimization algor...
In 2021, the Coordinated Science Laboratory CSL, an Interdisciplinary
Re...
Although dynamic games provide a rich paradigm for modeling agents'
inte...
As autonomous cars are becoming tangible technologies, road networks wil...
This paper proposes a data-driven method for learning convergent control...
In this paper, we study the problem of multiple stochastic agents intera...
We propose a method, based on empirical game theory, for a robot operati...
Many robotic applications involve interactions between multiple agents w...
Successful robotic operation in stochastic environments relies on accura...
Road network junctions, such as merges and diverges, often act as bottle...
In a traffic network, vehicles normally select their routes selfishly.
C...
It is known that connected and autonomous vehicles are capable of mainta...
Vehicle bypassing is known to negatively affect delays at traffic diverg...