  Planning in partially observable Markov decision processes (POMDPs) remains a
challenging topic in the artificial intelligence community, in spite of recent
impressive progress in approximation techniques. Previous research has
indicated that online planning approaches are promising in handling large-scale
POMDP domains efficiently as they make decisions "on demand" instead of
proactively for the entire state space. We present a Factored Hybrid Heuristic
Online Planning (FHHOP) algorithm for large POMDPs. FHHOP gets its power by
combining a novel hybrid heuristic search strategy with a recently developed
factored state representation. On several benchmark problems, FHHOP
substantially outperformed state-of-the-art online heuristic search approaches
in terms of both scalability and quality.
