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Evaluating Generalization in Multiagent Systems using Agent-Interaction Graphs

Published: 09 July 2018 Publication History

Abstract

Learning from interactions between agents is a key component for inference in multiagent systems. Depending on the downstream task, there could be multiple criteria for evaluating the generalization performance of learning. In this work, we propose a novel framework for evaluating generalization in multiagent systems based on agent-interaction graphs. An agent-interaction graph models agents as nodes and interactions as hyper-edges between participating agents. Using this abstract data structure, we define three notions of generalization for principled evaluation of learning in multiagent systems.

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Jacques Ferber. 1999. Multi-agent systems: An introduction to distributed artificial intelligence. Vol. Vol. 1. Addison-Wesley Reading.
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Aditya Grover, Maruan Al-Shedivat, Jayesh K. Gupta, Yura Burda, and Harrison Edwards. 2018. Learning Policy Representations in Multiagent Systems International Conference on Machine Learning.
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Michael Littman. 1994. Markov games as a framework for multi-agent reinforcement learning International Conference on Machine Learning.
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Peter Stone and Manuela Veloso. 2000. Multiagent systems: A survey from a machine learning perspective. Autonomous Robots, Vol. 8, 3 (2000), 345--383.
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Vladimir Vapnik. 2013. The nature of statistical learning theory. Springer science & business media.
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Ricardo Vilalta and Youssef Drissi . 2002. A perspective view and survey of meta-learning. Artificial Intelligence Review Vol. 18, 2 (2002), 77--95.
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Michael Wooldridge. 2009. An introduction to multiagent systems. John Wiley & Sons.

Cited By

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  • (2018)Learning in games with lossy feedbackProceedings of the 32nd International Conference on Neural Information Processing Systems10.5555/3327345.3327420(5140-5150)Online publication date: 3-Dec-2018

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cover image ACM Conferences
AAMAS '18: Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems
July 2018
2312 pages

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International Foundation for Autonomous Agents and Multiagent Systems

Richland, SC

Publication History

Published: 09 July 2018

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Author Tags

  1. agent-interaction graphs
  2. generalization
  3. multiagent systems

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  • Research-article

Conference

AAMAS '18
Sponsor:
AAMAS '18: Autonomous Agents and MultiAgent Systems
July 10 - 15, 2018
Stockholm, Sweden

Acceptance Rates

AAMAS '18 Paper Acceptance Rate 149 of 607 submissions, 25%;
Overall Acceptance Rate 1,155 of 5,036 submissions, 23%

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Cited By

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  • (2018)Learning in games with lossy feedbackProceedings of the 32nd International Conference on Neural Information Processing Systems10.5555/3327345.3327420(5140-5150)Online publication date: 3-Dec-2018

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