Summary of Count-based Novelty Exploration in Classical Planning, by Giacomo Rosa and Nir Lipovetzky
Count-based Novelty Exploration in Classical Planning
by Giacomo Rosa, Nir Lipovetzky
First submitted to arxiv on: 25 Aug 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 proposes a novel count-based exploration method for learning agents solving sequential decision problems. The authors draw inspiration from Novelty search, which has been successful in Classical Planning by recording the first occurrence of each tuple. However, existing methods require an exponential increase in the number of tuples considered as the search progresses. To address this, the paper introduces classical count-based novelty, which explores the state space with a constant number of tuples by leveraging the frequency of each tuple’s appearance in the search tree. The authors also introduce algorithmic contributions, including a trimmed open list that maintains a constant size by pruning nodes with poor novelty values. These techniques are shown to complement existing novelty heuristics when integrated into a classical solver, achieving competitive results in challenging benchmarks from recent International Planning Competitions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers solve problems by trying new things. It’s like when you’re playing a game and you need to figure out what to do next. The authors came up with a new way for computers to explore and try new things, which is important because it helps them solve problems better. They tested their idea on some challenging problems and found that it worked well. This could lead to even better computer programs in the future. |
Keywords
* Artificial intelligence * Pruning
