Scaling Observation-aware Planning in Uncertain Domains

TL;DR

This paper presents scalable symbolic techniques for solving sensor selection and positional observability problems in uncertain domains, significantly improving performance over previous methods.

Contribution

It introduces a new decomposition-based solving method for the OOP, enhancing scalability and efficiency in decision-making under uncertainty.

Findings

Performance improved by 3 to 5 orders of magnitude

Decomposition of POMDPs enables scalable solutions

New method outperforms previous parameter synthesis approaches

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.