Summary of Learning Generalized Policies For Fully Observable Non-deterministic Planning Domains, by Till Hofmann et al.
Learning Generalized Policies for Fully Observable Non-Deterministic Planning Domains
by Till Hofmann, Hector Geffner
First submitted to arxiv on: 3 Apr 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 In this paper, researchers develop a novel approach to learn general policies for solving complex planning problems in Fully Observable, Non-Deterministic (FOND) domains. Building upon existing methods for classical domains, the authors extend their formulations and combinatorial methods to tackle FOND planning problems. The approach is evaluated experimentally over various benchmark domains, showcasing the learned general policies that can be used as an alternative FOND planning method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us solve big planning puzzles by learning smart rules. Imagine you have a bunch of small training problems, and you want to use them to figure out how to solve bigger, similar problems. The researchers came up with a new way to do this for special kinds of planning problems called FOND domains. They tested it on some examples and showed that it works! It’s like having a superpower to find solutions in a special kind of puzzle space. |
