Human-Centered Explainable Artificial Intelligence for Marine Autonomous Surface Vehicles
Explainable Artificial Intelligence (XAI) for Autonomous Surface Vehicles (ASVs) addresses developers’ needs for model interpretation, understandability, and trust. As ASVs approach wide-scale deployment, these needs are expanded to include end user interactions in real-world contexts. Despite recen...
Saved in:
Main Authors: | Erik Veitch, Ole Andreas Alsos |
---|---|
Format: | article |
Language: | EN |
Published: |
MDPI AG
2021
|
Subjects: | |
Online Access: | https://doaj.org/article/a9ee8d00e35445d38f1f3ed8dc176a57 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Explaining a Deep Reinforcement Learning Docking Agent Using Linear Model Trees with User Adapted Visualization
by: Vilde B. Gjærum, et al.
Published: (2021) -
Motion Control of Autonomous Underwater Vehicle Based on Fractional Calculus Active Disturbance Rejection
by: Junhe Wan, et al.
Published: (2021) -
A Coordination System between Decision Making and Controlling for Autonomous Collision Avoidance of Large Intelligent Ships
by: Zhengyu Zhou, et al.
Published: (2021) -
Revealing the Impact of Global Heating on North Atlantic Circulation Using Transparent Machine Learning
by: Maike Sonnewald, et al.
Published: (2021) -
Game Theory for Unmanned Vehicle Path Planning in the Marine Domain: State of the Art and New Possibilities
by: Marco Cococcioni, et al.
Published: (2021)