Environment Shift Games: Are Multiple Agents the Solution, and not the Problem, to Non-Stationarity?

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

Machine learning and artificial intelligence models that interact with and in an environment will unavoidably have impact on this environment and change it. This is often a problem as many methods do not anticipate such a change in the environment and thus may start acting sub-optimally. Although efforts are made to deal with this problem, we believe that a lot of potential is unused. Driven by the recent success of predictive machine learning, we believe that in many scenarios one can predict when and how a change in the environment will occur. In this paper we introduce a blueprint that intimately connects this idea to the multiagent setting, showing that the multiagent community has a pivotal role to play in addressing the challenging problem of changing environments.
Original languageEnglish
Title of host publicationAAMAS '21: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages23-27
ISBN (Electronic)978-1-4503-8307-3
Publication statusPublished - 2021
Event20th International Conference on Autonomous Agentsand Multiagent Systems - Virtual/online event due to COVID-19
Duration: 3 May 20217 May 2021
Conference number: 20

Conference

Conference20th International Conference on Autonomous Agentsand Multiagent Systems
Abbreviated titleAAMAS 2021
Period3/05/217/05/21

Keywords

  • Non-Stationarity
  • Sequential Decision Making

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