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Revisiting dynamic constraint satisfaction for model-based planning

Published online by Cambridge University Press:  20 December 2016

Jeremy Frank
Jeremy Frank*
Affiliation:
NASA Ames Research Center, Moffett Field, CA, USA e-mail: jeremy.d.frank@nasa.gov
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Abstract

As planning problems become more complex, it is increasingly useful to integrate complex constraints on time and resources into planning models, and use constraint reasoning approaches to help solve the resulting problems. Dynamic constraint satisfaction is a key enabler of automated planning in the presence of such constraints. In this paper, we identify some limitations with the previously developed theories of dynamic constraint satisfaction. We identify a minimum set of elementary transformations from which all other transformations can be constructed. We propose a new classification of dynamic constraint satisfaction transformations based on a formal criteria, namely the change in the fraction of solutions. This criteria can be used to evaluate elementary transformations of a constraint satisfaction problem as well as sequences of transformations. We extend the notion of transformations to include constrained optimization problems. We discuss how this new framework can inform the evolution of planning models, automated planning algorithms, and mixed-initiative planning.

Type
Articles
Information
The Knowledge Engineering Review , Volume 31 , Special Issue 5: Constraint Satisfaction for Planning and Scheduling , November 2016 , pp. 429 - 439
Copyright
© Cambridge University Press, 2016 

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