Multi-objective multi-agent decision making: a utility-based analysis and survey

Roxana Rădulescu*, Patrick Mannion, Diederik M. Roijers, Ann Nowé

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

The majority of multi-agent system implementations aim to optimise agents’ policies with respect to a single objective, despite the fact that many real-world problem domains are inherently multi-objective in nature. Multi-objective multi-agent systems (MOMAS) explicitly consider the possible trade-offs between conflicting objective functions. We argue that, in MOMAS, such compromises should be analysed on the basis of the utility that these compromises have for the users of a system. As is standard in multi-objective optimisation, we model the user utility using utility functions that map value or return vectors to scalar values. This approach naturally leads to two different optimisation criteria: expected scalarised returns (ESR) and scalarised expected returns (SER). We develop a new taxonomy which classifies multi-objective multi-agent decision making settings, on the basis of the reward structures, and which and how utility functions are applied. This allows us to offer a structured view of the field, to clearly delineate the current state-of-the-art in multi-objective multi-agent decision making approaches and to identify promising directions for future research. Starting from the execution phase, in which the selected policies are applied and the utility for the users is attained, we analyse which solution concepts apply to the different settings in our taxonomy. Furthermore, we define and discuss these solution concepts under both ESR and SER optimisation criteria. We conclude with a summary of our main findings and a discussion of many promising future research directions in multi-objective multi-agent systems.

Original languageEnglish
Article number10
Pages (from-to)1-52
Number of pages52
JournalAutonomous Agents and Multi-Agent Systems
Volume34
Issue number1
Early online date9 Dec 2019
DOIs
Publication statusE-pub ahead of print - 9 Dec 2019

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Keywords

  • Multi-agent systems
  • Multi-objective decision making
  • Multi-objective optimisation criteria
  • Reinforcement learning
  • Solution concepts

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