Paper Info
Title
Vision for Decisions: Utilizing Uncertain Real-Time Information and Signaling for Conservation
Abstract
Recent advances in fields such as computer vision and natural language processing have created new opportunities for developing agents that can automatically interpret their environment. Concurrently, advances in artificial intelligence have made the coordination of many such agents possible. However, there is little work considering both the low-level reasoning that allows agents to interpret their environment, such as deep learning techniques, and the high-level reasoning that coordinates such agents. By considering both together, we can better handle real-world scenarios. We will describe a real-world deployment of conservation drones to illustrate this point.
Year
DOI
Venue
2020
10.5555/3398761.3399117
AAMAS '19: International Conference on Autonomous Agents and Multiagent Systems Auckland New Zealand May, 2020
DocType
ISBN
Citations 
Conference
978-1-4503-7518-4
0
PageRank 
References 
Authors
0.34
0
1
Name
Order
Citations
PageRank
Elizabeth Bondi113.07