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A utility-based analysis of equilibria in multi-objective normal-form games

Part of: Adaptive Learning Agents 2019

Published online by Cambridge University Press:  30 June 2020

Roxana Rădulescu ,
Patrick Mannion Open the ORCID record for Patrick Mannion [Opens in a new window] ,
Yijie Zhang ,
Diederik M. Roijers  and
Ann Nowé
Roxana Rădulescu
Affiliation:
Artificial Intelligence Lab, Vrije Universiteit Brussel, Pleinlaan 2, Brussels1050, Belgium, e-mails: [email protected], [email protected]
Patrick Mannion
Affiliation:
School of Computer Science, National University of Ireland Galway, GalwayH91 TK33, Ireland, e-mail: [email protected]
Yijie Zhang
Affiliation:
Universiteit van Amsterdam, Amsterdam, The Netherlands, e-mail: [email protected]
Diederik M. Roijers
Affiliation:
Artificial Intelligence Lab, Vrije Universiteit Brussel, Pleinlaan 2, Brussels1050, Belgium, e-mails: [email protected], [email protected] Microsystems Technology, HU University of Applied Sciences Utrecht, Heidelberglaan 15, 3584CSUtrecht, The Netherlands, e-mail: [email protected]
Ann Nowé
Affiliation:
Artificial Intelligence Lab, Vrije Universiteit Brussel, Pleinlaan 2, Brussels1050, Belgium, e-mails: [email protected], [email protected]
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Abstract

In multi-objective multi-agent systems (MOMASs), agents explicitly consider the possible trade-offs between conflicting objective functions. We argue that compromises between competing objectives in MOMAS should be analyzed on the basis of the utility that these compromises have for the users of a system, where an agent’s utility function maps their payoff vectors to scalar utility values. This utility-based approach naturally leads to two different optimization criteria for agents in a MOMAS: expected scalarized returns (ESRs) and scalarized expected returns (SERs). In this article, we explore the differences between these two criteria using the framework of multi-objective normal-form games (MONFGs). We demonstrate that the choice of optimization criterion (ESR or SER) can radically alter the set of equilibria in a MONFG when nonlinear utility functions are used.

Type
Adaptive and Learning Agents
Information
The Knowledge Engineering Review , Volume 35 , 2020 , e32
Copyright
© The Author(s), 2020. Published by Cambridge University Press

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Footnotes

*

This article extends an earlier unpublished paper (Rădulescu et al., 2019) that was originally presented at the Adaptive and Learning Agents Workshop 2019.

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