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Explainable AI Planning (XAIP): Overview and the Case of Contrastive Explanation (Extended Abstract)

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Reasoning Web. Explainable Artificial Intelligence

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11810))

Abstract

Model-based approaches to AI are well suited to explainability in principle, given the explicit nature of their world knowledge and of the reasoning performed to take decisions. AI Planning in particular is relevant in this context as a generic approach to action-decision problems. Indeed, explainable AI Planning (XAIP) has received interest since more than a decade, and has been taking up speed recently along with the general trend to explainable AI. In the lecture, we provide an overview, categorizing and illustrating the different kinds of explanation relevant in AI Planning; and we outline recent works on one particular kind of XAIP, contrastive explanation. This extended abstract gives a brief summary of the lecture, with some literature pointers. We emphasize that completeness is neither claimed nor intended; the abstract may serve as a brief primer with literature entry points.

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Notes

  1. 1.

    See the 2019 edition at https://kcl-planning.github.io/XAIP-Workshops/ICAPS_2019.

References

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Acknowledgments

This material is based upon work supported by the Air Force Office of Scientific Research under award number FA9550-18-1-0245. Jörg Hoffmann’s research group has received support by DFG grant 389792660 as part of TRR 248 (see https://perspicuous-computing.science). Daniele Magazzeni’s research group has received support by EPSRC grant EP/R033722/1: Trust in Human-Machine Partnerships.

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Authors and Affiliations

  1. Saarland Informatics Campus, Saarland University, Saarbrücken, Germany

    Jörg Hoffmann

  2. King’s College London, London, UK

    Daniele Magazzeni

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  1. Jörg Hoffmann
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  2. Daniele Magazzeni
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Correspondence to Jörg Hoffmann .

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Editors and Affiliations

  1. Technische Universität Dresden, Dresden, Sachsen, Germany

    Markus Krötzsch

  2. Bosch Center for Artificial Intelligence, Renningen, Germany

    Daria Stepanova

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Hoffmann, J., Magazzeni, D. (2019). Explainable AI Planning (XAIP): Overview and the Case of Contrastive Explanation (Extended Abstract). In: Krötzsch, M., Stepanova, D. (eds) Reasoning Web. Explainable Artificial Intelligence. Lecture Notes in Computer Science(), vol 11810. Springer, Cham. https://doi.org/10.1007/978-3-030-31423-1_9

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  • DOI: https://doi.org/10.1007/978-3-030-31423-1_9

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  • Online ISBN: 978-3-030-31423-1

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