The grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
The dynamics of reinforcement learning in cooperative multiagent systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Economic dynamics of agents in multiple auctions
Proceedings of the fifth international conference on Autonomous agents
Multiagent learning using a variable learning rate
Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Pricing in Agent Economies Using Multi-Agent Q-Learning
Autonomous Agents and Multi-Agent Systems
Pseudo-convergent Q-Learning by Competitive Pricebots
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Price Formation in Double Auctions
E-Commerce Agents, Marketplace Solutions, Security Issues, and Supply and Demand
Self-interested automated mechanism design and implications for optimal combinatorial auctions
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
Resource Allocation in the Grid Using Reinforcement Learning
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Pricing and Resource Allocation in Computational Grid with Utility Functions
ITCC '05 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II - Volume 02
Analyzing Market-Based Resource Allocation Strategies for the Computational Grid
International Journal of High Performance Computing Applications
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Dynamic pricing based on asymmetric multiagent reinforcement learning: Research Articles
International Journal of Intelligent Systems - Learning Approaches for Negotiation Agents and Automated Negotiation
Adaptive mechanism design: a metalearning approach
ICEC '06 Proceedings of the 8th international conference on Electronic commerce: The new e-commerce: innovations for conquering current barriers, obstacles and limitations to conducting successful business on the internet
Learning the task allocation game
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
A taxonomy of market-based resource management systems for utility-driven cluster computing
Software—Practice & Experience
Auctioning resources in Grids: model and protocols: Research Articles
Concurrency and Computation: Practice & Experience
Tycoon: An implementation of a distributed, market-based resource allocation system
Multiagent and Grid Systems
Reinforcement learning with utility-aware agents for market-based resource allocation
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Theoretical Advantages of Lenient Learners: An Evolutionary Game Theoretic Perspective
The Journal of Machine Learning Research
Predicting and preventing coordination problems in cooperative Q-learning systems
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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Market-based mechanisms offer a promising approach for distributed resource allocation. In this paper we consider the Iterative Price Adjustment, a pricing mechanism that can be used in commodity-market resource allocation systems. We address the scenario where agents use utility functions to describe preferences in the allocation and learn demand functions optimized for the market by Reinforcement Learning. In particular, we investigate reward functions based on the individual utilities of the agents and the Social Welfare of the market. We also evaluate the quality of demand functions obtained throughout the learning process with the aim of analyzing its influence on the behavior of the agents and exploring how much learning is enough, so the amount required can be reduced. This investigation shows that both reward functions deliver similar results when the market consists of only learning agents. We further investigate this behavior and present its theoretical-experimental explanation.