Summary of Odgr: Online Dynamic Goal Recognition, by Matan Shamir et al.
ODGR: Online Dynamic Goal Recognition
by Matan Shamir, Osher Elhadad, Matthew E. Taylor, Reuth Mirsky
First submitted to arxiv on: 23 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a new approach to Reinforcement Learning (RL), where the goal is not to optimize an agent’s behavior, but to recognize the goals of another agent in real-time. This Goal Recognition (GR) task has traditionally been framed as a planning problem, where one recognizes an agent’s objectives based on its observed actions. Recent RL-based approaches have shown promise for GR, but are limited to recognizing predefined goals and lack scalability in domains with many possible goals. To address these limitations, this paper introduces the concept of “Online Dynamic Goal Recognition” (ODGR), which allows for real-time recognition of changing and expansive environments. The authors demonstrate the feasibility of solving ODGR using transfer learning in a navigation domain, opening up new possibilities for robust GR methods that can adapt to dynamic environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about teaching machines to understand what other machines are trying to achieve. Usually, we want machines to make good decisions on their own, but this paper asks if it’s possible to teach machines to recognize what other machines are trying to do in real-time. This is important because it could help machines work better together and adapt to changing situations. The authors come up with a new way of doing this called “Online Dynamic Goal Recognition” (ODGR). They test their idea on a navigation task and show that it works, which means we might be able to use this approach in the future. |
Keywords
» Artificial intelligence » Reinforcement learning » Transfer learning
