Ricardo Aler and Daniel Borrajo
In the last decade, there has been a strong and increasing interest on building fast planning systems. From it, we have seen an enormous improvement in relation to the solvability horizon of current planning techniques. Most of these techniques rely on the translation of the predicate logic description of domains into propositional representations of them. Then, a fast search procedure is applied for obtaining solutions to planning problems. While this is an important step towards solving the planning problem, we believe older planners that are based on predicate logic computation can still be competitive when they are combined with learning capabilities. This is so, because they can acquire and use more abstract representations of control knowledge (CK) which are easier to describe and maintain by humans and/or automatic systems than the ones based on propositional logic. In this paper, we present the results we have obtained with a relatively "old" planner, Prodigy4.0, powered with CK that has been acquired using a mixed human-computer collaboration. We advocate for the initial generation of CK by a learning system, and its later refinement by a human. In fact, we can iterate this process until the desired result has been achieved. We show results in the logistics domain used at AIPS'00 that are at the same level when compared with other techniques in the Track on hand-tailored planning systems (Track2).