Name
Abstract | ||
|---|---|---|
Decision-making under uncertainty is a crucial ability for autonomous systems. In its most general form, this problem can be formulated as a partially observable Markov decision process (POMDP). The solution policy of a POMDP can be implicitly encoded as a value function. In partially observable settings, the value function is typically learned via forward simulation of the system evolution. Focus... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/LRA.2019.2903259 | IEEE Robotics and Automation Letters |
Keywords | Field | DocType |
Planning,Bidirectional control,Uncertainty,Safety,Navigation,Robot sensing systems | Mathematical optimization,Observable,Partially observable Markov decision process,Planner,Risk assessment,Bellman equation,Control engineering,Autonomous system (Internet),Engineering,Scalability | Journal |
Volume | Issue | ISSN |
4 | 3 | 2377-3766 |
Citations | PageRank | References |
1 | 0.35 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sung-Kyun Kim | 1 | 5 | 1.77 |
| Rohan Thakker | 2 | 4 | 3.78 |
| Ali-akbar Agha-mohammadi | 3 | 140 | 22.23 |