Summary of Policy-space Search: Equivalences, Improvements, and Compression, by Frederico Messa et al.
Policy-Space Search: Equivalences, Improvements, and Compression
by Frederico Messa, André Grahl Pereira
First submitted to arxiv on: 28 Mar 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 A fully-observable non-deterministic (FOND) planner like A* with Non-Determinism (AND) is a crucial component of artificial intelligence planning with uncertainty. AND generalizes A* for FOND planning by searching for a solution policy through an explicit heuristic search on the policy space of the FOND task. This paper improves the performance of AND’s policy-space search, presenting a polynomial-time procedure to construct a solution policy given only the set of states that should be mapped. The authors also introduce three concepts of equivalences between policies, which can prune part of the policy search space and make AND more effective in solving FOND tasks. Additionally, they explore the impact of considering structural state-space symmetries and satisficing techniques on policy detection and search efficiency. The authors apply a group theory technique to compute these symmetries and introduce a solution compressor that finds a compact representation of policies using the minimum number of partial states. As a result, AND* with these techniques can solve FOND tasks two orders of magnitude faster than before, making it competitive with other state-of-the-art FOND planners. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AND* is an AI planner for uncertain situations. It helps find the best way to solve problems by searching through many possible solutions. This paper makes AND* better at its job by creating a new way to search for solutions and by identifying when different solutions are actually the same. This lets AND* look at fewer possibilities and make decisions faster. The researchers also looked at how using special patterns in the problem and using shortcuts can help AND* solve problems even more quickly. |
