Before revision:  The partially observable Markov decision process (POMDP) offers a principled approach for planning under uncertainty. However, its computational complexitygrows exponentially with the planning horizon. We propose to use temporally-extended macro-actions  to cut down the effective planning horizon and thus the exponential factor of the complexity.
After revision:  The partially observable Markov decision process (POMDP) is a principled general framework for robot decision making under uncertainty, but POMDP planning suffers from high computational complexity, when long-term planning is required. While temporally-extended macro-actions to cut down the effective planning horizon and thus the exponential factor of the complexity.