Abstract
In this paper we present a general-purposed algorithm for transforming a planning problem specified in Strips into a concise state description for single state or symbolic exploration.
The process of finding a state description consists of four phases. In the first phase we symbolically analyze the domain specification to determine constant and one-way predicates, i.e. predicates that remain unchanged by all operators or toggle in only one direction, respectively.
In the second phase we symbolically merge predicates invariants which lead to a drastic reduction of state encoding size, while in the third phase we constrain the domains of the predicates to be considered by enumerating the operators of the planning problem. The fourth phase combines the result of the previous phases.
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Edelkamp, S., Helmert, M. (2000). Exhibiting Knowledge in Planning Problems to Minimize State Encoding Length. In: Biundo, S., Fox, M. (eds) Recent Advances in AI Planning. ECP 1999. Lecture Notes in Computer Science(), vol 1809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10720246_11
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DOI: https://doi.org/10.1007/10720246_11
Publisher Name: Springer, Berlin, Heidelberg
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