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Issue No. 10 - Oct. (2018 vol. 30)
ISSN: 1041-4347
pp: 1915-1928
Xu Lu , Institute of Computing Theory and Technology and State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, P.R. China
Cong Tian , Institute of Computing Theory and Technology and State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, P.R. China
Zhenhua Duan , Institute of Computing Theory and Technology and State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, P.R. China
Hongwei Du , Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, P.R. China
ABSTRACT
Knowledge based approaches developed for AI planning can convert an intractable planning problem to a tractable one. Current techniques often use temporal logics to express Search Control Knowledge (SCK) in logic based planning. However, traditional temporal logics are limited in expressiveness since they are unable to express spatial constraints which are as important as temporal ones in many planning domains. To this end, we propose a two-dimensional (spatial and temporal) logic namely PPTL$_$^{\mathrm{SL}}$_$ by temporalizing separation logic with PPTL (Propositional Projection Temporal Logic) which is well-suited to specify SCK involving both spatial and temporal constraints in planning. We prove that PPTL $_$^{\mathrm{SL}}$_$ is decidable essentially via an equisatisfiable translation from PPTL $_$^{\mathrm{SL}}$_$ to its restricted form. Moreover, we implement a tool, S-TSolver, which effectively computes plans under the guidance of the spatio-temporal SCK expressed by PPTL $_$^{\mathrm{SL}}$_$ formulas. The effectiveness of the tool is evaluated on selected benchmark domains from the International Planning Competition.
INDEX TERMS
Planning, Roads, Automobiles, Junctions, Search problems, Benchmark testing, Tools
CITATION

X. Lu, C. Tian, Z. Duan and H. Du, "Planning with Spatio-Temporal Search Control Knowledge," in IEEE Transactions on Knowledge & Data Engineering, vol. 30, no. 10, pp. 1915-1928, 2018.
doi:10.1109/TKDE.2018.2810144
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