Handling multiple objectives and multiple agents is an important and ubiquitous characteristic of many,
if not most, real-world decision problems. Mathematically, this translates to agents receiving a reward
vector, rather than a scalar reward. This seemingly minor change fundamentally transforms the problem,
shaping both the optimization criteria and the solution concepts. For example, the well-known
game-theory result that every (single-objective) normal form game has a Nash equilibrium, no longer
holds when the agents care about more than one objective.
In this tutorial, we will start with what it means to care about more than one aspect of the solution
and why it is pertinent for modelling multi-agent settings. We will examine what agents should optimise
for in multi-objective settings and discuss different assumptions, culminating in a taxonomy of
multi-objective multi-agent settings and the accompanying solution concepts. We will then follow up with
existing results and algorithmic approaches from evolutionary and multi-agent multi-objective
reinforcement learning.
Gaurav is a postdoctoral scholar at the Autonomous Agents and Distributed Intelligence Lab, at Oregon
State University. He earned his Ph.D. from Oregon State University in 2023. His work with the AI-CARING
Institute aims to facilitate collective decision-making required to pursue high-level, long-term,
dynamic, and possibly ill-defined objectives emerging from changing user preferences.
Roxana is an assistant professor at the Intelligent Systems group, Utrecht University. She obtained her
PhD degree at the Vrije Universiteit Brussel in September 2021. Her research is focussed on the
development of multi-agent decision making systems where each agent is driven by different objectives
and goals, under the paradigm of multi-objective multi-agent reinforcement learning.
Patrick is a Lecturer Above the Bar / Assistant Professor in the School of Computer Science at
University of Galway. He is also Programme Director of the PgCert in AI for Professionals at University
of Galway. He is Deputy Editor of The Knowledge Engineering Review, and an Editorial Board Member for
Neural Computing & Applications. He holds a BEng, a HDip and a PhD from University of Galway. His main
research interests are in multi-objective decision making, multi-agent systems and reinforcement
learning.