TY - JOUR
T1 - Loosely coupled formulations for automated planning
T2 - An integer programming perspective
AU - Van Den Briel, Menkes H L
AU - Vossen, Thomas
AU - Kambhampati, Subbarao
PY - 2008
Y1 - 2008
N2 - We represent planning as a set of loosely coupled network flow problems, where each network corresponds to one of the state variables in the planning domain. The network nodes correspond to the state variable values and the network arcs correspond to the value transitions. The planning problem is to find a path (a sequence of actions) in each network such that, when merged, they constitute a feasible plan. In this paper we present a number of integer programming formulations that model these loosely coupled networks with varying degrees of flexibility. Since merging may introduce exponentially many ordering constraints we implement a so-called branch-and-cut algorithm, in which these constraints are dynamically generated and added to the formulation when needed. Our results are very promising, they improve upon previous planning as integer programming approaches and lay the foundation for integer programming approaches for cost optimal planning.
AB - We represent planning as a set of loosely coupled network flow problems, where each network corresponds to one of the state variables in the planning domain. The network nodes correspond to the state variable values and the network arcs correspond to the value transitions. The planning problem is to find a path (a sequence of actions) in each network such that, when merged, they constitute a feasible plan. In this paper we present a number of integer programming formulations that model these loosely coupled networks with varying degrees of flexibility. Since merging may introduce exponentially many ordering constraints we implement a so-called branch-and-cut algorithm, in which these constraints are dynamically generated and added to the formulation when needed. Our results are very promising, they improve upon previous planning as integer programming approaches and lay the foundation for integer programming approaches for cost optimal planning.
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U2 - 10.1613/jair.2443
DO - 10.1613/jair.2443
M3 - Article
AN - SCOPUS:44449168150
SN - 1076-9757
VL - 31
SP - 217
EP - 257
JO - Journal of Artificial Intelligence Research
JF - Journal of Artificial Intelligence Research
ER -