HSVI One-Liner “impact of approximation decreases as steps from the root node” Novelty combined alpha-vector and forward heuristics to guide search of belief states before backup 100x times faster in PBVI scales to huge environments Goal: minimize “regret” (difference until optimal policy) Novelty HSVI 2 Projected the upper bound onto a convex hull (HSVI2: via approximate convex hull projection) uses blind lower bound Notable Methods Key Figs New Concepts Notes