Search Results for author:
Found 6 papers, 3 papers with code
People Attribute Purpose to Autonomous Vehicles When Explaining Their Behavior
A hallmark of a good XAI system is explanations that users can understand and act on.
Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review
One way to mitigate this challenge is to utilize explainable AI (XAI) techniques.
Causal Explanations for Sequential Decision-Making in Multi-Agent Systems
We present CEMA: Causal Explanations in Multi-Agent systems; a framework for creating causal natural language explanations of an agent's decisions in dynamic sequential multi-agent systems to build more trustworthy autonomous agents.
Bridging the Transparency Gap: What Can Explainable AI Learn From the AI Act?
To begin to bridge this gap, we overview and clarify the terminology of how XAI and European regulation -- the Act and the related General Data Protection Regulation (GDPR) -- view basic definitions of transparency.
Deep Reinforcement Learning for Multi-Agent Interaction
The development of autonomous agents which can interact with other agents to accomplish a given task is a core area of research in artificial intelligence and machine learning.
GRIT: Fast, Interpretable, and Verifiable Goal Recognition with Learned Decision Trees for Autonomous Driving
As autonomous driving is safety-critical, it is important to have methods which are human interpretable and for which safety can be formally verified.
Autonomous Driving
Robotics
Multiagent Systems
