Summary of Structure and Reduction Of Mcts For Explainable-ai, by Ronit Bustin and Claudia V. Goldman
Structure and Reduction of MCTS for Explainable-AI
by Ronit Bustin, Claudia V. Goldman
First submitted to arxiv on: 10 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Information Theory (cs.IT)
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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 research paper proposes a novel approach to sequential decision-making planning problems with infinite state spaces, inspired by AlphaZero algorithms. The authors train a neural model using Monte Carlo Tree Search, showing its applicability to real-life planning scenarios. To facilitate explainability, the study focuses on extracting information from the Monte Carlo Tree Search data structure. Novel methods are presented for simplifying and reducing this data, enabling the construction of human-understandable explanations. Theoretical and algorithmic aspects are explored, with examples demonstrating the potential applications in building explanations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us solve complex planning problems by training a neural model that thinks ahead about different possible futures. To make these plans more understandable to humans, we need to know why the computer chose certain decisions. This research focuses on the information stored within the Monte Carlo Tree Search algorithm, which contains clues about the decision-making process. The study shows new ways to simplify this information and extract key insights that can be used to explain the planner’s choices. |
