Summary of Learning Logic Specifications For Policy Guidance in Pomdps: An Inductive Logic Programming Approach, by Daniele Meli et al.
Learning Logic Specifications for Policy Guidance in POMDPs: an Inductive Logic Programming Approach
by Daniele Meli, Alberto Castellini, Alessandro Farinelli
First submitted to arxiv on: 29 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Logic in Computer Science (cs.LO)
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 The paper proposes a novel approach to learning domain-dependent policy heuristics for Partially Observable Markov Decision Processes (POMDPs) using Inductive Logic Programming (ILP). The authors develop a method to generate interpretable belief-based policy specifications from POMDP traces of executions and demonstrate its effectiveness on two challenging problems, rocksample and pocman. By leveraging ILP, the learned heuristics expressed in Answer Set Programming (ASP) outperform neural networks and handcrafted task-specific heuristics while requiring less computational time. The methodology shows promise for scaling POMDPs to complex realistic domains with many actions and long planning horizons. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us better understand how we can make decisions when the situation is uncertain. It proposes a new way to learn good rules for making these decisions using data from past experiences. This approach is tested on two difficult problems and shows that it can work well, even in situations that are not exactly like those used to train the system. |
