Persistent patterns: Multi-agent learning beyond equilibrium and utility

Georgios Piliouras, Carlos Nieto-Granda, Henrik I. Christensent, Jeff S. Shamma

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

20 Scopus citations

Abstract

We propose an analytic framework for multi-agent learning that, unlike standard approaches, is not connected to convergence to an equilibrium concept nor to payoff guarantees for the agents. We view multi-agent systems as reservoirs that allow for the long term survival of rich spatiotemporal correlations (i.e., patterns) amongst the agents' behaviors. Our aim is to develop abstractions that allow us to capture details about the possible limit behaviors of such systems. Our approach is based on the contrast between weakly and strongly persistent properties. Informally, a property is weakly persistent if for each starting point there exist limit points that satisfy it. A property is strongly persistent if it is satisfied by all limit points. In the case of non-converging dynamics the set of weakly persistent properties can be significantly richer than that of the strongly persistent properties reflecting topological properties of the system limit sets in a concise and algorithmically tractable manner.

Original languageEnglish (US)
Title of host publication13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages181-188
Number of pages8
ISBN (Electronic)9781634391313
StatePublished - Jan 1 2014
Event13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014 - Paris, France
Duration: May 5 2014May 9 2014

Publication series

Name13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014
Volume1

Other

Other13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014
CountryFrance
CityParis
Period05/5/1405/9/14

Keywords

  • Dynamical systems
  • Game theory
  • Information theory
  • Multi-agent systems
  • Replicator dynamics
  • Topology

ASJC Scopus subject areas

  • Artificial Intelligence

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