Projecten per jaar
Samenvatting
We present a detailed analysis of Nash equilibria in multi-objective normal-form games, which are normal-form games with vectorial payoffs. Our approach is based on modelling each player's utility using a utility function that maps a vector to a scalar utility. For mixed strategies, we can apply the utility function before calculating the expectation of the payoff vector as well as after, resulting in two distinct optimisation criteria. We show that when computing the utility from the expected payoff, a Nash equilibrium can be guaranteed when players have quasiconcave utility functions. In addition, we show that when players have quasiconvex utility functions, pure strategy Nash equilibria are equal under both optimisation criteria. We extend this to settings where some players optimise for one criterion, while others optimise for the second. We combine these results and formulate an algorithm that computes all pure strategy Nash equilibria given quasiconvex utility functions.
Originele taal-2 | English |
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Titel | The 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023) |
Uitgeverij | International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS) |
Pagina's | 269-271 |
Aantal pagina's | 3 |
ISBN van elektronische versie | 978-1-4503-9432-1 |
Status | Published - mei 2023 |
Evenement | The 22nd International Conference on Autonomous Agents and Multiagent Systems - London, United Kingdom Duur: 29 mei 2023 → 2 jun. 2023 https://aamas2023.soton.ac.uk |
Conference
Conference | The 22nd International Conference on Autonomous Agents and Multiagent Systems |
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Verkorte titel | AAMAS 2023 |
Land/Regio | United Kingdom |
Stad | London |
Periode | 29/05/23 → 2/06/23 |
Internet adres |
Vingerafdruk
Duik in de onderzoeksthema's van 'A Study of Nash Equilibria in Multi-Objective Normal-Form Games: JAAMAS Track'. Samen vormen ze een unieke vingerafdruk.Projecten
- 2 Actief
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FWOTM1108: Besluitvorming in multi-objective multi-agent domeinen met teambeloning
1/10/22 → 28/02/27
Project: Fundamenteel
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FWOTM1082: Reinforcement Learning in Multi-Doel Multi-Agent Systemen
1/11/21 → 31/10/25
Project: Fundamenteel