Technical Note: \cal Q-Learning
Machine Learning
Multiagent systems: a modern approach to distributed artificial intelligence
Multiagent systems: a modern approach to distributed artificial intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Target Reaching by Using Visual Information and Q-learning Controllers
Autonomous Robots
A Roadmap of Agent Research and Development
Autonomous Agents and Multi-Agent Systems
Introduction to Discrete Event Systems
Introduction to Discrete Event Systems
Model-free learning control of neutralization processes using reinforcement learning
Engineering Applications of Artificial Intelligence
Multi-agent Reinforcement Learning Using Strategies and Voting
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 02
Multiscale Three-Phase Flow Simulation Dedicated to Model Based Control
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part II
Holonic-Based Environment for Solving Transportation Problems
HoloMAS '09 Proceedings of the 4th International Conference on Industrial Applications of Holonic and Multi-Agent Systems: Holonic and Multi-Agent Systems for Manufacturing
Automatica (Journal of IFAC)
Requirement specification for agent-based cooperative control of dynamical systems
CDVE'10 Proceedings of the 7th international conference on Cooperative design, visualization, and engineering
Comparing multiagent systems research in combinatorial auctions and voting
Annals of Mathematics and Artificial Intelligence
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume PartI
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In this paper a system for monitoring a biotechnical system is presented. Some of the events associated with the state of metabolic reactions are indistinguishable, mainly due to lack of appropriate sensors and measurement capabilities. Therefore, a solution is needed to identify the state in which the reactor currently is, based on the information and measurements available in real time. The solution presented in this paper is based on a multi agent system, in which particular agents identify the state of the process based on selected measurements. Those partial identification results are than used to provide a cumulative result by means of a voting mechanism between all the particular agents. Such a solution seems to be a promising alternative to standard monitoring and identification methods.