Agendas for Multi-Agent Learning

Abstract

Shoham et al. [1] identify several important agendas which can help direct research in multi-agent learning. We propose two additional agendas called modelling and design which cover the problems we need to consider before our agents can start learning. We then consider research goals for modelling, design, and learning, and identify the problem of finding learning algorithms that guarantee convergence to Pareto-dominant equilibria against a wide range of opponents. Finally, we conclude with an example: starting from an informally-specified multi-agent learning problem, we illustrate how one might formalize and solve it by stepping through the tasks of modelling, design, and learning. This report is an extended version of a paper which will appear in a special issue of Artificial Intelligence Journal [2]; in addition to the topics covered in that paper, this report contains several appendices providing extra details on various algorithms, definitions, and examples.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2006
Accession Number
ADA462725

Entities

People

  • Geoffrey J. Gordon

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy
  • Human Systems

DTIC Thesaurus Topics

  • Abstracts
  • Agreements
  • Algorithms
  • Artificial Intelligence
  • Assembly
  • Assembly Lines
  • Bargaining
  • Computer Science
  • Homosexuality
  • Learning
  • Machine Learning
  • Matrix Games
  • Negotiations
  • Optimization
  • Social Welfare
  • Supervised Machine Learning
  • Supply Chain Management

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Operations Research
  • Technical Research and Report Writing.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms