Learning about other agents in a dynamic multiagent system

Authors:
Junling Hu;Michael P. Weliman
Affiliations:
Simon School of Business, University of Rochester, Rochester, NY 14627, USA;Computer Science & Engineering, University of Michigan, Ann Arbor, MI 48109-2110, USA
Venue:
Cognitive Systems Research
Year:
2001

Citing 6
Cited 11

Conjectural Equilibrium in Multiagent Learning

Machine Learning
Correlating Internal Parameters and External Performance: Learning Soccer Agents

ECAI '96 Selected papers from the Workshop on Distributed Artificial Intelligence Meets Machine Learning, Learning in Multi-Agent Environments
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm

ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Experimental Results on Q-Learning for General-Sum Stochastic Games

ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems

ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A decision-theoretic approach to coordinating multiagent interactions

IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1

Automated index management for distributed web search

CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Applying case-based reasoning and multi-agent intelligent system to context-aware comparative shopping

Decision Support Systems
One Dynamic Pricing Strategy in Agent Economy Using Neural Network Based on Online Learning

WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Multi-agent system approach to context-aware coordinated web services under general market mechanism

Decision Support Systems
A layered approach to learning coordination knowledge in multiagent environments

Applied Intelligence
A fuzzy constraint-based agent negotiation with opponent learning

ACOS'07 Proceedings of the 6th Conference on WSEAS International Conference on Applied Computer Science - Volume 6
Exchange market for complex commodities: search for optimal matches

Journal of Experimental & Theoretical Artificial Intelligence
Bounded rationality via recursion

Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Multi-agent system approach to context-aware coordinated web services under general market mechanism

Decision Support Systems
Modeling opponent's beliefs via fuzzy constraint-directed approach in agent negotiation

ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
Learning opponent's beliefs via fuzzy constraint-directed approach to make effective agent negotiation

Applied Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

We analyze the problem of learning about other agents in a class of dynamic multiagent systems, where performance of the primary agent depends on behavior of the others. We consider an online version of the problem, where agents must learn models of the others in the course of continual interactions. Various levels of recursive models are implemented in a simulated double auction market. Our experiments show learning agents on average outperform non-learning agents who do not use information about others. Among learning agents, those with minimum recursion assumption generally perform better than the agents with more complicated, though often wrong assumptions.