Multi-objective multi-agent decision making: a utility-based analysis and survey
Roijers, Diederik M.
MetadataShow full item record
This item's downloads: 216 (view details)
Cited 10 times in Scopus (view citations)
Rădulescu, Roxana, Mannion, Patrick, Roijers, Diederik M., & Nowé, Ann. (2019). Multi-objective multi-agent decision making: a utility-based analysis and survey. Autonomous Agents and Multi-Agent Systems, 34(1), 10. doi: 10.1007/s10458-019-09433-x
The majority of multi-agent system implementations aim to optimise agents policies with respect to a single objective, despite the fact that many real-world problem domains are inherently multi-objective in nature. Multi-objective multi-agent systems (MOMAS) explicitly consider the possible trade-offs between conflicting objective functions. We argue that, in MOMAS, such compromises should be analysed on the basis of the utility that these compromises have for the users of a system. As is standard in multi-objective optimisation, we model the user utility using utility functions that map value or return vectors to scalar values. This approach naturally leads to two different optimisation criteria: expected scalarised returns (ESR) and scalarised expected returns (SER). We develop a new taxonomy which classifies multi-objective multi-agent decision making settings, on the basis of the reward structures, and which and how utility functions are applied. 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. Starting from the execution phase, in which the selected policies are applied and the utility for the users is attained, we analyse which solution concepts apply to the different settings in our taxonomy. Furthermore, we define and discuss these solution concepts under both ESR and SER optimisation criteria. We conclude with a summary of our main findings and a discussion of many promising future research directions in multi-objective multi-agent systems.
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Ireland
Showing items related by title, author, creator and subject.
Multi-priority Multi-path Selection for Video Streaming in Wireless Multimedia Sensor Networks Zhang, Lin; Hauswirth, Manfred; Shu, Lei; Zhou, ZhangBing; Reynolds, Vinny (Springer, 2008)Video sensors are used in wireless multimedia sensor networks (WMSNs) to enhance the capability for event description. Due to the limited transmission capacity of sensor nodes, a single path often cannot meet the ...
Knowledge-based multi-objective multi-agent reinforcement learning Mannion, Patrick (2017-08-17)Multi-Agent Reinforcement Learning (MARL) is a powerful Machine Learning paradigm, where multiple autonomous agents can learn to improve the performance of a system through experience. The majority of MARL implementations ...
Development of a Probabilistic Multi-Zone Multi-Source Computational Model for Indoor Air Pollution Exposure Assessment McGrath, James (2014-02-12)Airborne Particulate Matter (PM) is a major environmental concern because of its known impacts on human health, and since the developed world population spends approximately 89% of its time indoors, PM in the indoor ...