Scaling Observation-aware Planning in Uncertain Domains

2026-05-21

AI Summary

This work addresses the optimal observability problem (OOP) in uncertain environments, which entails balancing task feasibility against sensing costs. Focusing on its decidable subproblems—sensor selection (SSP) and position observability (POP)—the paper proposes a novel solution framework based on POMDP decomposition, integrating parameter synthesis with a symbolic–subsymbolic hybrid approach. This method dramatically improves computational efficiency, scaling solvable instances by three orders of magnitude and reducing runtime by five orders of magnitude compared to prior techniques. Consequently, the approach substantially expands the tractable boundary of observability-aware planning in partially observable settings.

Abstract

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.

Problem

• Optimal Observability Problem
• Sensor Selection Problem
• Positional Observability Problem
• POMDP
• Uncertain Domains

Innovation

• POMDP decomposition
• sensor selection
• observability optimization
• symbolic planning
• uncertain domains