Scaling Observation-aware Planning in Uncertain Domains

When designing autonomous agents for uncertain environments, engineers face a difficult trade-off: they must balance the need for high-quality data to achieve tasks against the significant costs of hardware and processing power. This research addresses the Optimal Observability Problem (OOP), which uses the Partially Observable Markov Decision Process (POMDP) model to determine the best sensing capabilities for an agent. The authors present new (sub-)symbolic techniques designed to make solving this problem more efficient and scalable.

Addressing Decidable Fragments

The researchers focus on two specific, decidable fragments of the Optimal Observability Problem: the Sensor Selection Problem (SSP) and the Positional Observability Problem (POP). By targeting these specific areas, the team aims to provide practical solutions for complex decision-making scenarios where traditional methods struggle to handle the computational load.

A New Approach via Decomposition

Beyond refining existing methods based on parameter synthesis, the authors introduce a novel solving technique. This method identifies effective observation functions by decomposing POMDPs into smaller, more manageable parts. This structural breakdown allows the system to navigate the complexity of uncertain domains more effectively than previous approaches.

Significant Performance Gains

The proposed method demonstrates substantial improvements in computational efficiency. According to the study, the new approach improves performance by 3 orders of magnitude regarding the size of the instances it can handle, and by 5 orders of magnitude in terms of total runtime. These results suggest that the decomposition strategy is a highly effective way to scale observation-aware planning for real-world applications.