# Scaling Observation-aware Planning in Uncertain Domains

arXiv: https://arxiv.org/abs/2605.22364 · HTML: https://mlsanity.com/paper/2605.22364/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 27 references that are in the map.
- First posted on arXiv 21 May 2026.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [The Probabilistic Model Checker Storm](https://mlsanity.com/paper/2002.07080/) (2020)
- [Parameter Synthesis in Markov Models: A Gentle Survey](https://mlsanity.com/paper/2207.06801/) (2022)
- [Inductive Synthesis of Finite-State Controllers for POMDPs](https://mlsanity.com/paper/2203.10803/) (2022)
- [Gradient-Descent for Randomized Controllers under Partial Observability](https://mlsanity.com/paper/2111.04407/) (2021)
- [Under-Approximating Expected Total Rewards in POMDPs](https://mlsanity.com/paper/2201.08772/) (2022)
- [Permissive Finite-State Controllers of POMDPs using Parameter Synthesis](https://mlsanity.com/paper/1710.10294/) (2017)
- [Sensor Synthesis for POMDPs with Reachability Objectives](https://mlsanity.com/paper/1710.00675/) (2017)
- [What should be observed for optimal reward in POMDPs?](https://mlsanity.com/paper/2405.10768/) (2024)
- [Constrained and Robust Policy Synthesis with Satisfiability-Modulo-Probabilistic-Model-Checking](https://mlsanity.com/paper/2511.08078/) (2025)
- [Towards SMT Solver Stability via Input Normalization](https://mlsanity.com/paper/2410.22419/) (2024)

## Abstract (arXiv)

Deciding which sensing capabilities to deploy on an agent in uncertain domains is a fundamental engineering challenge, in which one balances task achievability against the high costs of hardware and processing. This problem has previously been formalized as the Optimal Observability Problem (OOP), based on the well-known Partially Observable Markov Decision Process (POMDP) model for decision-making. This work studies (sub-)symbolic techniques to scale solving of decidable fragments of the OOP, namely the Sensor Selection Problem (SSP) and the Positional Observability Problem (POP). Besides improving the original approach based on parameter synthesis, we develop a new solving method that identifies sensible observation functions via decomposition of POMDPs, improving performance by 3 and 5 orders of magnitude for instance size and runtime, respectively.