TY - JOUR
T1 - Gradient-based mixed planning with symbolic and numeric action parameters
AU - Jin, Kebing
AU - Zhuo, Hankz Hankui
AU - Xiao, Zhanhao
AU - Wan, Hai
AU - Kambhampati, Subbarao
N1 - Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/12
Y1 - 2022/12
N2 - Dealing with planning problems with both logical relations and numeric changes in real-world dynamic environments is challenging. Existing numeric planning systems for the problem often discretize numeric variables or impose convex constraints on numeric variables, which harms the performance when solving problems. In this paper, we propose a novel algorithm framework to solve numeric planning problems mixed with logical relations and numeric changes based on gradient descent. We cast the numeric planning with logical relations and numeric changes as an optimization problem. Specifically, we extend syntax to allow parameters of action models to be either objects or real-valued numbers, which enhances the ability to model real-world numeric effects. Based on the extended modeling language, we propose a gradient-based framework to simultaneously optimize numeric parameters and compute appropriate actions to form candidate plans. The gradient-based framework is composed of an algorithmic heuristic module based on propositional operations to select actions and generate constraints for gradient descent, an algorithmic transition module to update states to next ones, and a loss module to compute loss. We repeatedly minimize loss by updating numeric parameters and compute candidate plans until it converges into a valid plan for the planning problem. In the empirical study, we exhibit that our algorithm framework is both effective and efficient in solving planning problems mixed with logical relations and numeric changes, especially when the problems contain obstacles and non-linear numeric effects.
AB - Dealing with planning problems with both logical relations and numeric changes in real-world dynamic environments is challenging. Existing numeric planning systems for the problem often discretize numeric variables or impose convex constraints on numeric variables, which harms the performance when solving problems. In this paper, we propose a novel algorithm framework to solve numeric planning problems mixed with logical relations and numeric changes based on gradient descent. We cast the numeric planning with logical relations and numeric changes as an optimization problem. Specifically, we extend syntax to allow parameters of action models to be either objects or real-valued numbers, which enhances the ability to model real-world numeric effects. Based on the extended modeling language, we propose a gradient-based framework to simultaneously optimize numeric parameters and compute appropriate actions to form candidate plans. The gradient-based framework is composed of an algorithmic heuristic module based on propositional operations to select actions and generate constraints for gradient descent, an algorithmic transition module to update states to next ones, and a loss module to compute loss. We repeatedly minimize loss by updating numeric parameters and compute candidate plans until it converges into a valid plan for the planning problem. In the empirical study, we exhibit that our algorithm framework is both effective and efficient in solving planning problems mixed with logical relations and numeric changes, especially when the problems contain obstacles and non-linear numeric effects.
KW - AI planning
KW - Mixed planning
UR - http://www.scopus.com/inward/record.url?scp=85139231688&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85139231688&partnerID=8YFLogxK
U2 - 10.1016/j.artint.2022.103789
DO - 10.1016/j.artint.2022.103789
M3 - Article
AN - SCOPUS:85139231688
SN - 0004-3702
VL - 313
JO - Artificial Intelligence
JF - Artificial Intelligence
M1 - 103789
ER -