Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
C4.5: programs for machine learning
C4.5: programs for machine learning
The power of sampling in knowledge discovery
PODS '94 Proceedings of the thirteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Neural networks and intellect: using model-based concepts
Neural networks and intellect: using model-based concepts
Machine Learning
Multiagent teamwork: analyzing the optimality and complexity of key theories and models
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Is Sampling Useful in Data Mining? A Case in the Maintenance of Discovered Association Rules
Data Mining and Knowledge Discovery
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Tuning the Collaboration Level with Autonomous Agents: A Principled Theory
AI*IA 01 Proceedings of the 7th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
A Unified Framework for Modeling Cooperative Design Processes and Cooperative Business Processes
HICSS '98 Proceedings of the Thirty-First Annual Hawaii International Conference on System Sciences-Volume 5 - Volume 5
Task Allocation: A Group Self-Design Approach
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
Stochastic Resonance Neural Network and Its Performance
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 2 - Volume 2
Comparing market and token-based coordination
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
The communicative multiagent team decision problem: analyzing teamwork theories and models
Journal of Artificial Intelligence Research
On the approximation of stochastic processes by approximate identity neural networks
IEEE Transactions on Neural Networks
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Due to computational intractability, large scale coordination algorithms are necessarily heuristic and hence require tuning for particular environments. In domains where characteristics of the environment vary dramatically from scenario to scenario, it is desirable to have automated techniques for appropriately configuring the coordination. This paper presents an approach that takes performance data from a simulator to train a stochastic neural network that concisely models the complex, probabilistic relationship between configurations, environments and performance metrics. The stochastic neural network is used as the core of a tool that allows rapid online or offline configuration of coordination algorithms to particular scenarios and user preferences. The overall system allows rapid adaptation of coordination, leading to better performance in new scenarios.