Summary of What Should Be Observed For Optimal Reward in Pomdps?, by Alyzia-maria Konsta et al.
What should be observed for optimal reward in POMDPs?
by Alyzia-Maria Konsta, Alberto Lluch Lafuente, Christoph Matheja
First submitted to arxiv on: 17 May 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 novel approach to optimizing the observation capabilities of partially observable Markov Decision Processes (POMDPs) is presented in this paper, which focuses on selecting an agent’s sensors cost-effectively to achieve desired goals. The authors introduce the optimal observability problem (OOP), where a POMDP’s observation capabilities are modified within a fixed budget to minimize expected reward below a given threshold. While the OOP is undecidable in general, a decidable fragment is identified and two algorithms are proposed for solving this problem: one based on optimal strategies of the underlying Markov decision process and another using parameter synthesis with SMT. The authors demonstrate promising results for variants of typical POMDP examples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary POMDPs help machines make decisions in uncertain situations, but what if we can control what information they get? In this research, scientists tackle a new problem: how to choose the best sensors for an agent so it achieves its goals while staying within a budget. They call this the “optimal observability problem.” They show that solving this problem is tricky, but they find ways to do it for certain types of strategies. Two algorithms are proposed to solve this problem, and tests on classic examples in the field show promising results. |
