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Conclusions

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Data-driven Generation of Policies

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

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Abstract

The AI planning literature contains decades of substantial work on discovering sequences of actions that lead to a given outcome that, similar to this work, is often specified as a goal condition.

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Authors and Affiliations

  1. Department of Computer Science, University of Maryland, College Park, MD, USA

    Austin Parker

  2. Department of Computer Science, University of Oxford, Oxford, UK

    Gerardo I. Simari

  3. Charles River Analytics Inc., Cambridge, MA, USA

    Amy Sliva

  4. Computer Science Department, University of Maryland, College Park, MD, USA

    V. S. Subrahmanian

Authors
  1. Austin Parker
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  2. Gerardo I. Simari
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  3. Amy Sliva
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  4. V. S. Subrahmanian
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Parker, A., Simari, G.I., Sliva, A., Subrahmanian, V.S. (2014). Conclusions. In: Data-driven Generation of Policies. SpringerBriefs in Computer Science. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-0274-3_6

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  • DOI: https://doi.org/10.1007/978-1-4939-0274-3_6

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4939-0273-6

  • Online ISBN: 978-1-4939-0274-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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