Propagating Piecewise-Linear Weights in Temporal Networks

Authors

  • Luke Hunsberger Vassar College
  • Roberto Posenato Université degli Studi di Verona

Abstract

This paper presents a novel technique using piecewise-linear functions (PLFs) as weights on edges in the graphs of two kinds of temporal networks to solve several previously open problems. Generalizing constraint-propagation rules to accommodate PLF weights requires implementing a small handful of functions. Most problems are solved by inserting one or more edges with an initial weight of δ (a variable), then using the modified rules to propagate the PLF weights. For one kind of network, a new set of propagation rules is introduced to avoid a non-termination issue that arises when propagating PLF weights. The paper also presents two new results for determining the tightest horizon that can be imposed while preserving a network’s dynamic consistency/controllability.

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Published

2021-05-25

How to Cite

Hunsberger, L., & Posenato, R. (2021). Propagating Piecewise-Linear Weights in Temporal Networks. Proceedings of the International Conference on Automated Planning and Scheduling, 29(1), 223-231. Retrieved from https://ojs.aaai.org/index.php/ICAPS/article/view/3480