Search Results for author:
Found 5 papers, 2 papers with code
AI of Brain and Cognitive Sciences: From the Perspective of First Principles
Nowadays, we have witnessed the great success of AI in various applications, including image classification, game playing, protein structure analysis, language translation, and content generation.
Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed Traffic
On-ramp merging is a challenging task for autonomous vehicles (AVs), especially in mixed traffic where AVs coexist with human-driven vehicles (HDVs).
Autonomous Driving using Safe Reinforcement Learning by Incorporating a Regret-based Human Lane-Changing Decision Model
The predicted decisions are incorporated in the safety constraints for reinforcement learning in training and in implementation.
Respect Your Emotion: Human-Multi-Robot Teaming based on Regret Decision Model
This work studies the human-like characteristics brought by regret emotion in one-human-multi-robot teaming for the application of domain search.
A Human-Computer Interface Design for Quantitative Measure of Regret Theory
The key of obtaining a quantitative model of regret theory is to measure the preference in humans' mind when they choose among a set of options.
Human-Computer Interaction
