Loosely Coupled Formulations for Automated Planning: An Integer Programming Perspective

TL;DR

This paper introduces a novel integer programming approach to automated planning by modeling it as loosely coupled network flow problems, enabling flexible and efficient plan generation.

Contribution

It presents new integer programming formulations for planning as coupled network flows and a branch-and-cut algorithm to handle complex ordering constraints.

Findings

Improved planning performance over previous integer programming methods.

Dynamic generation of ordering constraints enhances scalability.

Lays groundwork for cost-optimal planning using integer programming.

Abstract

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.