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Modern high energy and nuclear physics experiments require sophisticated control systems which are crucial to successful operation of an experiment. Advanced control is a key enabling technology underpinning enhanced scientific data production quality, and higher safety margins. Success in designing complex control systems depends on taking a holistic viewpoint. Current market is full of quality control gadget/system suppliers providing high quality, ready key control solutions, suitable for specific system components of the experiment (for e.g. gas control system, high voltage control systems, data readout systems, etc). Successful complex experiment control system cannot be guaranteed by simply using commercially available high quality sub-control system components. Commercially available control systems require different interfaces and communication protocols. In the recent years many industrial, proprietary control systems, with their specific hardware and software solutions, were being used in high energy and nuclear physics experiments. Inevitably control and data production systems for the future experiments will face problems in areas such as interoperability, scalability, and standard user interface. Interoperability and coordination of these system components in side of the unified control environment, and under control of robust control state machine is the answer.

Agent Framework for Experiment Control Systems (AFECS) is developed to ease design and deployment of large scale, hierarchical experiment control systems. The unique feature which sets this framework apart from conventional control systems is its incorporation of intelligent agent concepts.

Control Oriented Ontology Language

The ever growing heterogeneity of physics experiment control systems presents a real challenge to uniformly

describe control system components and their operational details. Control Oriented Ontology Language (COOL) is a meta-data modeling language that provides generic means for representation of physics experiment control processes and components, and their relationships, rules and axioms. It provides a semantic reference frame that is useful for automating the communication of information for configuration, deployment and operation. Additionally, COOL provides precise specification of software and hardware components. Specifically, COOL presents relevant set of ontology concepts along with the relationships among them in order to describe experiment control components and generic event-based state machines. COOL has been successfully used to develop a complete and dynamic knowledge base for experiment control systems, developed using the AFECS framework.