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

01/17/2020
by   Roxana Rădulescu, et al.
0

In multi-objective multi-agent systems (MOMAS), agents explicitly consider the possible tradeoffs between conflicting objective functions. We argue that compromises between competing objectives in MOMAS should be analysed 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 optimisation criteria for agents in a MOMAS: expected scalarised returns (ESR) and scalarised expected returns (SER). 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 optimisation criterion (ESR or SER) can radically alter the set of equilibria in a MONFG when non-linear utility functions are used.

READ FULL TEXT
research
09/06/2019

Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey

The majority of multi-agent system (MAS) implementations aim to optimise...
research
11/14/2020

Opponent Learning Awareness and Modelling in Multi-Objective Normal Form Games

Many real-world multi-agent interactions consider multiple distinct crit...
research
12/13/2021

On Nash Equilibria in Normal-Form Games With Vectorial Payoffs

We provide an in-depth study of Nash equilibria in multi-objective norma...
research
11/17/2021

Preference Communication in Multi-Objective Normal-Form Games

We study the problem of multiple agents learning concurrently in a multi...
research
01/13/2023

Bridging the Gap Between Single and Multi Objective Games

A classic model to study strategic decision making in multi-agent system...
research
10/16/2012

Multi-objective Influence Diagrams

We describe multi-objective influence diagrams, based on a set of p obje...
research
07/01/2022

Multi-Objective Coordination Graphs for the Expected Scalarised Returns with Generative Flow Models

Many real-world problems contain multiple objectives and agents, where a...

Please sign up or login with your details

Forgot password? Click here to reset