Toward a self-learning governance loop for competitive multi-attribute MAS


Pernpeintner, Michael


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URL: https://madoc.bib.uni-mannheim.de/59649
Additional URL: https://dl.acm.org/doi/abs/10.5555/3463952.3464179
URN: urn:nbn:de:bsz:180-madoc-596492
Document Type: Conference or workshop publication
Year of publication: 2021
Book title: AAMAS '21: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems : Richland, SC, Virtual Event United Kingdom, May, 2021
Page range: 1619-1621
Conference title: AAMAS 2021
Location of the conference venue: Online
Date of the conference: 03.-07.05.2021
Publisher: Endriss, Ulle ; Nowé, Anne ; Dignum, Frank ; Lomuscio, Alessio
Place of publication: Richland, SC
Publishing house: International Foundation for Autonomous Agents and Multiagent Systems
ISBN: 978-1-4503-8307-3
Related URLs:
Publication language: English
Institution: Außerfakultäre Einrichtungen > Institut für Enterprise Systems (InES)
Subject: 004 Computer science, internet
Keywords (English): Restriction , Competition , Governance, Multi-Agent System
Abstract: Competitive Multi-Agent Systems (MAS) are inherently hard to control due to agent autonomy and strategic behavior, which is particularly problematic when there are system-level objectives to be achieved or specific environmental states to be avoided.Existing methods mostly assume specific knowledge about agent preferences, utilities and strategies, neglecting the fact that actions are not always directly linked to genuine agent preferences, but can also reflect anticipated competitor behavior, be a concession to a superior adversary or simply be intended to mislead other agents. This assumption both reduces applicability to real-world systems and opens room for manipulation.We therefore propose a new governance approach for Multi-Attribute MAS which relies exclusively on publicly observable actions and transitions, and uses the acquired knowledge to purposefully restrict action spaces, thereby achieving the system's objectives while preserving a high level of autonomy for the agents.

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