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Robust Explanations for Human-Neural Multi-agent Systems with Formal Verification

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Multi-Agent Systems (EUMAS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14282))

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Abstract

The quality of explanations in human-agent interactions is fundamental to the development of trustworthy AI systems. In this paper we study the problem of generating robust contrastive explanations for human-neural multi-agent systems and introduce two novel verification-based algorithms to (i) identify non-robust explanations generated by other methods and (ii) generate contrastive explanations equipped with formal robustness certificates. We present an implementation and evaluate the effectiveness of the approach on two case studies involving neural agents trained on credit scoring and traffic sign recognition tasks.

Work partially supported by the DARPA Assured Autonomy programme (FA8750-18-C-0095), the UK Royal Academy of Engineering (CiET17/18-26) and an Imperial College Research Fellowship awarded to Leofante. This paper is an extended version of [26] presented at AAMAS 2023.

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Authors and Affiliations

  1. Department of Computing, Imperial College London, London, UK

    Francesco Leofante & Alessio Lomuscio

Authors
  1. Francesco Leofante
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  2. Alessio Lomuscio
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Correspondence to Francesco Leofante .

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Editors and Affiliations

  1. Télécom Paris, Paris, France

    Vadim Malvone

  2. Universitá degli Studi di Napoli Federico II, Naples, Italy

    Aniello Murano

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Leofante, F., Lomuscio, A. (2023). Robust Explanations for Human-Neural Multi-agent Systems with Formal Verification. In: Malvone, V., Murano, A. (eds) Multi-Agent Systems. EUMAS 2023. Lecture Notes in Computer Science(), vol 14282. Springer, Cham. https://doi.org/10.1007/978-3-031-43264-4_16

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  • DOI: https://doi.org/10.1007/978-3-031-43264-4_16

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