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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.
|Number of pages||21|
|Journal||The Knowledge Engineering Review|
|Publication status||Published - 2020|
|Event||2019 Adaptive Learning Agents (ALA) workshop: Workshop of the AAMAS conference - Montreal, Canada|
Duration: 13 May 2019 → 14 May 2019
Radulescu, R., Mannion, P., Roijers, D. & Nowe, A., 2019, Proceedings of the Adaptive and Learning Agents Workshop 2019 (ALA-19) at AAMAS. 9 p.
Research output: Chapter in Book/Report/Conference proceeding › Conference paperOpen Access