Jennings_Accountability_Ascription_in_Multiagent_Teams.pdf (311.62 kB)
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Applying strategic reasoning for accountability ascription in multiagent teams

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conference contribution
posted on 05.11.2021, 11:19 authored by Vahid Yazdanpanah, Sebastian Stein, EH Gerding, Nick JenningsNick Jennings
For developing human-centred trustworthy autonomous systems and ensuring their safe and effective integration with the society, it is crucial to enrich autonomous agents with the capacity to represent and reason about their accountability. This is, on one hand, about their accountability as collaborative teams and, on the other hand, their individual degree of accountability in a team. In this context, accountability is understood as being responsible for failing to deliver a task that a team was allocated and able to fulfil. To that end, the semantic (strategic reasoning) machinery of the Alternating-time Temporal Logic (ATL) is a natural modelling approach as it captures the temporal, strategic, and coalitional dynamics of the notion of accountability. This allows focusing on the main problem on: “Who is accountable for an unfulfilled task in multiagent teams: when, why, and to what extent?” We apply ATL-based semantics to define accountability in multiagent teams and develop a fair and computationally feasible procedure for ascribing a degree of accountability to involved agents in accountable teams. Our main results are on decidability, fairness properties, and computational complexity of the presented accountability ascription methods in multiagent teams.

Funding

UKRI Trustworthy Autonomous Systems Hub

Engineering and Physical Sciences Research Council

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AutoTrust: Designing a Human-Centered Trusted, Secure, Intelligent and Usable Internet of Vehicles

Engineering and Physical Sciences Research Council

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Turing AI Fellowship: Citizen-Centric AI Systems

Engineering and Physical Sciences Research Council

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History

Published in

Proceedings of the Workshop on Artificial Intelligence Safety 2021 co-located with the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI 2021)

Pages

18

Source

AISafety 2021. Artificial Intelligence Safety 2021

Publisher

CEUR-WS.org

Version

VoR (Version of Record)

Rights holder

Copyright © 2021 for the individual papers by the papers' authors. Copyright © 2021 for the volume as a collection by its editors

Publisher statement

This is an Open Access Article. It is published by CEUR-WS.org under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

Publication date

2021-08-31

Copyright date

2021

ISSN

1613-0073

Book series

CEUR Workshop Proceedings vol. 2916

Language

en

Editor(s)

Huáscar Espinoza; John McDermid; Xiaowei Huang; Mauricio Castillo-Effen; Xin Cynthia Chen; José Hernández-Orallo; Seán Ó hÉigeartaigh; Richard Mallah; Gabriel Pedroza

Depositor

Deposit date: 4 November 2021

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