Vector-space Analysis of Belief-state Approximation for POMDPs
This work addresses computational efficiency in POMDPs for decision-making applications, though it is incremental as it builds on prior value-directed models.
The paper tackled the problem of belief state approximation in POMDPs by proposing new search procedures based on vector-space analysis, which run up to two orders of magnitude faster than existing methods while maintaining similar decision quality in practice.
We propose a new approach to value-directed belief state approximation for POMDPs. The value-directed model allows one to choose approximation methods for belief state monitoring that have a small impact on decision quality. Using a vector space analysis of the problem, we devise two new search procedures for selecting an approximation scheme that have much better computational properties than existing methods. Though these provide looser error bounds, we show empirically that they have a similar impact on decision quality in practice, and run up to two orders of magnitude more quickly.