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On Generating Explainable Plans with Assumption-Based Argumentation

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PRIMA 2018: Principles and Practice of Multi-Agent Systems (PRIMA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11224))

Abstract

Planning is a classic problem in Artificial Intelligence (AI). Recently, the need for creating “Explainable AI” has been recognised and voiced by many researchers. Leveraging on the strength of argumentation, in particular, the Related Admissible semantics for generating explanations, this work makes an initial step towards “explainable planning”. We illustrate (1) how plan generation can be equated to constructing acceptable arguments and (2) how explanations for both “planning solutions” as well as “invalid plans” can be obtained by extracting information from an arguing process. We present an argumentation-based model which takes plans written in a STRIPS-like language as its inputs and returns Assumption-based Argumentation (ABA) frameworks as its outputs. The presented plan construction mapping is both sound and complete in that the planning problem has a solution if and only if its corresponding ABA framework has a set of Related Admissible arguments with the planning goal as its topic. We use the classic Tower of Hanoi puzzle as our case study and demonstrate how ABA can be used to solve this planning puzzle while giving explanations.

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Notes

  1. 1.

    \(\tau \notin \mathcal {L}\) represents “true” and stands for the empty body of rules.

  2. 2.

    Here, a stands for ‘abstract’. Also, ‘proponent’ and ‘opponent’ should be seen as roles/fictitious participants in a debate rather than actual agents.

  3. 3.

    In the original definition of abstract dispute tree [7], every \(\mathbf{O}\) node is required to have exactly one child. We incorporate this requirement into the definition of admissible dispute tree given later, so that our notion of admissible abstract dispute tree and the admissible abstract dispute trees of  [7] coincide.

  4. 4.

    This is a standard approach in planning as it allows the complete specification of the planning search space. Techniques have been developed to estimate the step bound, see e.g. [17].

  5. 5.

    We use the convention that the over-line on \(\mathtt {A}\) denotes that \(\mathtt {\overline{A}}\) represents a list of variables of unspecified length. Variables without over-lines are “normal” variables.

  6. 6.

    \(\mathtt {mv,cl}\) and \(\mathtt {sm}\) are short-hands for \(\mathtt {move, clear}\) and \(\mathtt {smaller}\), respectively.

  7. 7.

    When defining ABA frameworks, we omit to indicate the language component, as this can be easily inferred from the other components (being the set of all sentences occurring in rules, assumptions, and contraries). Also, we use rule schemata to simplify the notation. Each rule schema represents the set of grounded rules.

  8. 8.

    \(\mathtt {hA}\) is shorthand for \(\mathtt {hasAct}\).

  9. 9.

    Throughout, \(\_\) stands for an anonymous variable.

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Authors and Affiliations

  1. Swansea University, Swansea, UK

    Xiuyi Fan

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  1. Xiuyi Fan
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Correspondence to Xiuyi Fan .

Editor information

Editors and Affiliations

  1. School of Computing and Information Systems, University of Melbourne, Melbourne, Australia

    Tim Miller

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

    Nir Oren

  3. Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan

    Yuko Sakurai

  4. Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan

    Itsuki Noda

  5. University of Otago, Dunedin, New Zealand

    Bastin Tony Roy Savarimuthu

  6. Computer Science Department, New Mexico State University, Las Cruces, USA

    Tran Cao Son

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Fan, X. (2018). On Generating Explainable Plans with Assumption-Based Argumentation. In: Miller, T., Oren, N., Sakurai, Y., Noda, I., Savarimuthu, B.T.R., Cao Son, T. (eds) PRIMA 2018: Principles and Practice of Multi-Agent Systems. PRIMA 2018. Lecture Notes in Computer Science(), vol 11224. Springer, Cham. https://doi.org/10.1007/978-3-030-03098-8_21

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  • DOI: https://doi.org/10.1007/978-3-030-03098-8_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03097-1

  • Online ISBN: 978-3-030-03098-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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