2012/03/15 by Gabriel Corona, Corona, Gabriel, François Charpillet +2
Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Formal Methods in Verification #Machine Learning and Algorithms #Reinforcement Learning in Robotics #cs.AI
paper · pdf · doi:10.48550/arxiv.1203.3474
Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI2010)
arxiv created 2012/03/15 · openalex publication_date 2012/03/15 · arxiv updated 2012/03/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose a new point-based method for approximate planning in Dec-POMDP which outperforms the state-of-the-art approaches in terms of solution quality. It uses a heuristic estimation of the prior probability of beliefs to choose a bounded number of policy trees: this choice is formulated as a combinatorial optimisation problem minimising the error induced by pruning.