Abstract
One approach to optimal planning is to first start with a sub- optimal solution as a seed plan, and then iteratively search for shorter plans. This approach inevitably leads to an increase in the size of the model to be solved. We introduce a reformulation of the planning problem in which the problem is described as a meta-CSP, which controls the search of an underlying SAT solver. Our results show that this approach solves a greater number of problems than both Maxplan and Blackbox, and our analysis discusses the advantages and disadvantages of searching in the backwards direction.
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Gregory, P., Long, D., Fox, M. (2007). A Meta-CSP Model for Optimal Planning. In: Miguel, I., Ruml, W. (eds) Abstraction, Reformulation, and Approximation. SARA 2007. Lecture Notes in Computer Science(), vol 4612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73580-9_17
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DOI: https://doi.org/10.1007/978-3-540-73580-9_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73579-3
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