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Plan Selection Framework for Policy-Aware Autonomous Agents

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Logics in Artificial Intelligence (JELIA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14281))

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Abstract

This paper proposes a framework for representing and reasoning about the plan selection process of an autonomous agent that is expected to operate within the boundaries of a given policy. We assume that the agent takes into consideration both policy compliance and plan length, and may prioritize one of these aspects over the other, based on circumstances. We consider authorization and obligation policies specified in the language \(\mathcal{AOPL}\) by Gelfond and Lobo. Our framework builds upon the AAA agent architecture and is implemented in ASP.

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Notes

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    Available at https://tinyurl.com/4exs9ens.

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Authors and Affiliations

  1. Miami University, Oxford, OH, 45056, USA

    Charles Harders & Daniela Inclezan

Authors
  1. Charles Harders
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  2. Daniela Inclezan
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Corresponding author

Correspondence to Daniela Inclezan .

Editor information

Editors and Affiliations

  1. TU Dresden, Dresden, Germany

    Sarah Gaggl

  2. Artificial Intelligence Research Institute - IIIA CSIC, Barcelona, Spain

    Maria Vanina Martinez

  3. Umeå University, Umeå, Sweden

    Magdalena Ortiz

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Harders, C., Inclezan, D. (2023). Plan Selection Framework for Policy-Aware Autonomous Agents. In: Gaggl, S., Martinez, M.V., Ortiz, M. (eds) Logics in Artificial Intelligence. JELIA 2023. Lecture Notes in Computer Science(), vol 14281. Springer, Cham. https://doi.org/10.1007/978-3-031-43619-2_43

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  • DOI: https://doi.org/10.1007/978-3-031-43619-2_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43618-5

  • Online ISBN: 978-3-031-43619-2

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