Decision Making in Multi-Objective Multi-Agent Systems - A Utility-Based Perspective

Research output: ThesisPhD Thesis


The prevalence of artificial agents in our world raises the need to ensure
that they are able to handle the salient properties of the environment, in
order to plan or learn how to solve specific tasks.
A first important aspect is the fact that real-world problems are not
restricted to one agent, and often involve multiple agents acting in the same
environment. Such settings have already proven to be challenging to solve,
with a few examples including traffic systems, electricity grids, or warehouse
management. Furthermore, the majority of these multi-agent system
implementations aim to optimise the agents’ behaviour with respect to a
single objective, despite the fact that many problem domains inherently
involve multiple objectives. By taking a multi-objective perspective on
decision-making problems, complex trade-offs can be managed; e.g., supply
chain management involves a complex coordination process for optimising
the information and material flow between all the components of the supply
chain, while minimising overall costs and complying with the conflicting
demands of the involved partners.
In this work, we focus on these highlighted aspects and discuss how the
process of decision-making and learning of artificial agents can be formalised
and approached when there are multiple agents involved, and multiple
objectives that need to be considered in the process. To analyse such
problems, we adopt a utility-based perspective, and advocate that
compromises between competing objectives should be made on the basis of
the utility that these compromises have for the users, in other words, it
should depend on the desirability of the outcomes.
As a first contribution, we develop a novel taxonomy to classify these
settings. 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.
As a second contribution, we proceed to analyse and investigate game
theoretic equilibria under different multi-objective optimisation criteria and
provide theoretical results concerning the existence and conditions for
arriving to such solutions in these scenarios. We additionally show that it is
possible for Nash equilibria to not exist in certain multi-objective multi-agent
As a final contribution, we present the first study of the effects of
opponent modelling on multi-objective multi-agent interactions. We
contribute novel reinforcement learning algorithms for this setting, along
with extensions that incorporate opponent behaviour reconstruction and
learning with opponent learning awareness (i.e., learning while anticipating
one's impact on the opponent's learning step)
Translated title of the contributionDecision Making in Multi-Objective Multi-Agent Systems - A Utility-Based Perspective
Original languageDutch
QualificationDoctor of Sciences
Awarding Institution
  • Vrije Universiteit Brussel
  • Nowe, Ann, Supervisor
Award date3 Sep 2021
Place of PublicationBrussels
Publication statusPublished - 2021


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