Summary of Depth-bounded Epistemic Planning, by Thomas Bolander et al.
Depth-Bounded Epistemic Planning
by Thomas Bolander, Alessandro Burigana, Marco Montali
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 algorithm for epistemic planning based on dynamic epistemic logic (DEL). The key innovation is that the algorithm limits its reasoning depth to an upper bound b, allowing it to reason about higher-order knowledge up to modal depth b. The algorithm uses canonical b-bisimulation contraction to ensure unique minimal models and guarantee soundness. Additionally, the algorithm is shown to be complete for planning tasks with solutions within bound b of reasoning depth, making it fixed-parameter tractable in the number of agents and atoms. Two variants are presented: a tree search and graph search algorithm, which are benchmarked against a baseline epistemic planner. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way for computers to plan using a type of logic called dynamic epistemic logic (DEL). The key idea is that the computer can only think about things at most a certain number of levels deep. This helps it figure out what’s possible and what’s not, which is important for planning. The algorithm uses special tricks to make sure it gets the right answer and it’s very good at solving problems that require thinking ahead. It even has two different ways of doing this: one that works well for big trees and another that’s better for complicated graphs. |
