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Planning with Regression Analysis in Transaction Logic

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Web Reasoning and Rule Systems (RR 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9209))

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Abstract

Heuristic search is arguably the most successful paradigm in Automated Planning, which greatly improves the performance of planning strategies. However, adding heuristics usually leads to very complicated planning algorithms. In order to study different properties (e.g. completeness) of those complicated planning algorithms, it is important to use an appropriate formal language and framework. In this paper, we argue that Transaction Logic is just such a specification language, which lets one formally specify both the heuristics, the planning algorithm, and also facilitates the discovery of more general and more efficient algorithms. To illustrate, we take the well-known regression analysis mechanism and show that Transaction Logic lets one specify the concept of regression analysis easily and thus express \(\textit{RSTRIPS}\), a classical and very complicated planning algorithm based on regression analysis. Moreover, we show that extensions to that algorithm that allow indirect effects and action ramification are obtained almost for free. Finally, a compact and clear logical formulation of the algorithm lets us prove the completeness of \(\textit{RSTRIPS}\)—a result that, to the best of our knowledge, has not been known before.

This work was supported, in part, by the NSF grant 0964196.

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Notes

  1. 1.

    Requiring all variables in \(Pre_{\alpha }\) to occur in \(\{X_1,...,X_n\}\) is not essential: we can easily extend our framework and consider the extra variables to be existentially quantified.

  2. 2.

    We simply write \( \mathfrak {R}(\alpha ,\ell ) \) whenever L just contains a single literal \( \ell \).

  3. 3.

    http://ewl.cewit.stonybrook.edu/planning/RSTRIPS-TR-full.pdf.

  4. 4.

    http://ewl.cewit.stonybrook.edu/planning/RSTRIPS-TR-full.pdf.

  5. 5.

    http://ewl.cewit.stonybrook.edu/planning/RSTRIPS-TR-full.pdf.

  6. 6.

    http://ewl.cewit.stonybrook.edu/planning/RSTRIPS-TR-full.pdf.

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Acknowledgments

We are thankful to the anonymous referees for their thorough reviews and suggestions.

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Authors and Affiliations

  1. Stony Brook University, Stony Brook, NY, 11794, USA

    Reza Basseda & Michael Kifer

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  1. Reza Basseda
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  2. Michael Kifer
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Correspondence to Reza Basseda .

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Editors and Affiliations

  1. University of California, Santa Cruz, USA

    Balder ten Cate

  2. National University of Ireland, Galway, Ireland

    Alessandra Mileo

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Basseda, R., Kifer, M. (2015). Planning with Regression Analysis in Transaction Logic. In: ten Cate, B., Mileo, A. (eds) Web Reasoning and Rule Systems. RR 2015. Lecture Notes in Computer Science(), vol 9209. Springer, Cham. https://doi.org/10.1007/978-3-319-22002-4_5

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  • DOI: https://doi.org/10.1007/978-3-319-22002-4_5

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