Introduction to Multiagent Systems
Introduction to Multiagent Systems
A Roadmap of Agent Research and Development
Autonomous Agents and Multi-Agent Systems
A Practitioners' Review of Industrial Agent Applications
Autonomous Agents and Multi-Agent Systems
Industrial Adoption of Agent-Based Technologies
IEEE Intelligent Systems
Developing Multi-Agent Systems with JADE (Wiley Series in Agent Technology)
Developing Multi-Agent Systems with JADE (Wiley Series in Agent Technology)
Industrial deployment of multi-agent technologies: review and selected case studies
Autonomous Agents and Multi-Agent Systems
Actions and social interactions in multi-agent systems
Knowledge and Information Systems
MAS-Based Cooperative Control for Biotechnological Process-A Case Study
HoloMAS '09 Proceedings of the 4th International Conference on Industrial Applications of Holonic and Multi-Agent Systems: Holonic and Multi-Agent Systems for Manufacturing
Cooperative Control of Distributed Multi-Agent Systems
Cooperative Control of Distributed Multi-Agent Systems
Simulation approach for detection of the self-sustained oscillations in continuous culture
MCBE'10/MCBC'10 Proceedings of the 11th WSEAS international conference on mathematics and computers in business and economics and 11th WSEAS international conference on Biology and chemistry
Multi-agent oriented integration in distributed control system
KES-AMSTA'11 Proceedings of the 5th KES international conference on Agent and multi-agent systems: technologies and applications
Cooperative operating control for induction or elimination of self-sustained oscillations in CSTB
CDVE'11 Proceedings of the 8th international conference on Cooperative design, visualization, and engineering
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Application of the Agent and Multiagent Systems (AMAS) in the industrial continuous processes can be a quite interesting and effective solution, especially for monitoring and controlling purposes of bioreactor systems. In the classical approach, a process operator controls the process, but sometimes must take some essential decisions concerning the choice of control strategy. In the case of biological processes, due to their highly nonlinear nature, this can be quite difficult task. For instance, the oscillatory behavior of the bioreactor may lead to higher or lower average biomass concentrations. Hence, there is a need to support the operator by measuring and controlling some additional parameters and this cannot be achieved using only measuring devices and classical control algorithms alone. Based on the agent technology, it has been shown that it is possible to support the operator and to achieve process goals.