Abstract
In AI planning community, planning domains with derived predicates are very challenging to many planning system. Derived predicate is a new application of domain rules and domain knowledge acquisition. In this paper, we propose an approach to planning with derived predicates: defining activation sets of a derived predicate which are unrelated to any specific state and computing them in the preprocess phase through the instantiation rule-graph; replacing a derived predicate with one of its activation sets in relax-plan to extract action sequences. And we also implement the proposed approach in a new planner, called FF-DP, which shows good performance in our experiments.
This research is funded by Chinese Natural Science Foundation (60173039).
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Jiang, Zh., Jiang, Yf. (2006). Planning with Domain Rules Based on State-Independent Activation Sets. In: Hoffmann, A., Kang, Bh., Richards, D., Tsumoto, S. (eds) Advances in Knowledge Acquisition and Management. PKAW 2006. Lecture Notes in Computer Science(), vol 4303. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11961239_22
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DOI: https://doi.org/10.1007/11961239_22
Publisher Name: Springer, Berlin, Heidelberg
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