Market-based control: a paradigm for distributed resource allocation
Market-based control: a paradigm for distributed resource allocation
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
JCAT: a platform for the TAC market design competition
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems: demo papers
Market-based control of computational systems: introduction to the special issue
Autonomous Agents and Multi-Agent Systems
Co-learning segmentation in marketplaces
ALA'11 Proceedings of the 11th international conference on Adaptive and Learning Agents
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We propose a novel method for allocating multi-attribute computational resources via competing marketplaces. Trading agents, working on behalf of resource consumers and providers, choose to trade in resource markets where the resources being traded best align with their preferences and constraints. Market-exchange agents, in competition with each other, attempt to provide resource markets that attract traders, with the goal of maximising their profit. Because exchanges can only partially observe global supply and demand schedules, novel strategies are required to automate their search for market niches. Novel attribute-level selection (ALS) strategies are empirically analysed in simulated competitive market environments, and results suggest that using these strategies, market-exchanges can seek out market niches under a variety of environmental conditions.