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2016 Volume 31
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RESEARCH ARTICLE   Open Access    

Revisiting dynamic constraint satisfaction for model-based planning

  • Moffett Field

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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.
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  • Cite this article

    Jeremy Frank. 2016. Revisiting dynamic constraint satisfaction for model-based planning. The Knowledge Engineering Review 31(5)429−439, doi: 10.1017/S0269888916000242
    Jeremy Frank. 2016. Revisiting dynamic constraint satisfaction for model-based planning. The Knowledge Engineering Review 31(5)429−439, doi: 10.1017/S0269888916000242
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RESEARCH ARTICLE   Open Access    

Revisiting dynamic constraint satisfaction for model-based planning

  • Moffett Field

  • Published online: 20 December 2016
The Knowledge Engineering Review  31,  2016, 31(5): 429−439  |  Cite this article

Abstract: 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.

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    • The author would like to thank the anonymous reviewers of the paper for their feedback and editorial comments.

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    Jeremy Frank. 2016. Revisiting dynamic constraint satisfaction for model-based planning. The Knowledge Engineering Review 31(5)429−439, doi: 10.1017/S0269888916000242
    Jeremy Frank. 2016. Revisiting dynamic constraint satisfaction for model-based planning. The Knowledge Engineering Review 31(5)429−439, doi: 10.1017/S0269888916000242
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