Summary of Action Model Learning with Guarantees, by Diego Aineto et al.
Action Model Learning with Guarantees
by Diego Aineto, Enrico Scala
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 explores action model learning with full observability using the learning by search paradigm from Mitchell. The authors develop a theory for version spaces that interprets the task as searching for hypotheses consistent with learning examples. They instantiate their findings in an online algorithm maintaining a compact representation of all solutions, focusing on sound and complete models approximating the actual transition system. The paper shows how to manipulate output to build deterministic and non-deterministic formulations, proving convergence to the true model given enough examples. Experiments demonstrate the effectiveness of these formulations over various planning domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how computers can learn about actions they can take in different situations. The authors create a way for computers to search for rules that explain what happens when certain actions are taken. They test their idea and find that it works well on different problems, like planning routes or scheduling tasks. |
