We present PbP2, an automated system that generates efficient domain-specific multi planners from a portfolio of domain-independent planning techniques by (i) computing some sets of macro-actions for every planner in the portfolio, (ii) optimizing the parameter setting of the parameterized planners in the portfolio, (iii) selecting a promising combination of planners in the portfolio and relative useful macro-actions, and (iv) defining some running time slots for their round-robin scheduling during planning. The configuration of the portfolio yielding the multi planner relies on some knowledge about the performance of the planners and relative macro actions, which is automatically generated from a training problem set. PbP2 is a revision and extension of a preliminary version of this system (PbP) that was awarded at the learning track of IPC-2008.
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