Axiomatic foundation of the analytic hierarchy process
Management Science
Technical Note: \cal Q-Learning
Machine Learning
An adaptive agent bidding strategy based on stochastic modeling
Proceedings of the third annual conference on Autonomous Agents
High-performance bidding agents for the continuous double auction
Proceedings of the 3rd ACM conference on Electronic Commerce
Predicting Application Run Times Using Historical Information
IPPS/SPDP '98 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
A proportional share resource allocation algorithm for real-time, time-shared systems
RTSS '96 Proceedings of the 17th IEEE Real-Time Systems Symposium
A Fuzzy-Logic Based Bidding Strategy for Autonomous Agents in Continuous Double Auctions
IEEE Transactions on Knowledge and Data Engineering
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Learning-Based Negotiation Strategies for Grid Scheduling
CCGRID '06 Proceedings of the Sixth IEEE International Symposium on Cluster Computing and the Grid
The Penn-Lehman Automated Trading Project
IEEE Intelligent Systems
On-line evolutionary computation for reinforcement learning in stochastic domains
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Tycoon: An implementation of a distributed, market-based resource allocation system
Multiagent and Grid Systems
If multi-agent learning is the answer, what is the question?
Artificial Intelligence
Multiagent learning is not the answer. It is the question
Artificial Intelligence
Autonomous Bidding Agents: Strategies and Lessons from the Trading Agent Competition (Intelligent Robotics and Autonomous Agents)
Multi-agent Learning Dynamics: A Survey
CIA '07 Proceedings of the 11th international workshop on Cooperative Information Agents XI
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Learning and multiagent reasoning for autonomous agents
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Agent-human interactions in the continuous double auction
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Exploring bidding strategies for market-based scheduling
Decision Support Systems - Special issue: The fourth ACM conference on electronic commerce
Workload analysis of a cluster in a grid environment
JSSPP'05 Proceedings of the 11th international conference on Job Scheduling Strategies for Parallel Processing
Three automated stock-trading agents: a comparative study
AAMAS'04 Proceedings of the 6th AAMAS international conference on Agent-Mediated Electronic Commerce: theories for and Engineering of Distributed Mechanisms and Systems
A multi-level scheduler for batch jobs on grids
The Journal of Supercomputing
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The application of autonomous agents by the provisioning and usage of computational resources is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic resource provisioning and usage of computational resources, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems.The contributions of the paper are threefold. First, we present a framework for supporting consumers and providers in technical and economic preference elicitation and the generation of bids. Secondly, we introduce a consumer-side reinforcement learning bidding strategy which enables rational behavior by the generation and selection of bids. Thirdly, we evaluate and compare this bidding strategy against a truth-telling bidding strategy for two kinds of market mechanisms --- one centralized and one decentralized.