We describe a procedure that takes a classical planning task, a forward-search state, and a set of abstraction-based admissible heuristics, and derives an optimal additive composition of these heuristics with respect to the given state. Most importantly, we show that this procedure is polynomial-time for arbitrary sets of all known to us abstraction-based heuristics such as PDBs, constrained PD...

We study the complexity of sequentially-optimal classical planning, and discover new problem classes for whose such optimization is tractable. The results are based on exploiting numerous structural characteristics of planning problems, and a constructive proof technique that connects between certain tools from planning and tractable constraint optimization. In particular, we believe that struc...