IRAR: Smart Intention Recognition and Action Recommendation for Cyber-Physical Industry Environments

In this article we propose a generic framework for ontological intention recognition and action recommendation involving distributed and networked active digital object memories (ADOMe) for production factories and their components as well as individually manufactured products. For this purpose, a concrete and complex application scenario in the industrial environment is designed. In addition, the developed approach provides an important contribution to the utilization of ADOMes and their networking and intelligent representation of consumption, savings and savings potential. Furthermore, the processes and tasks of the proactive intention recognition component reflect the behavior and interaction of man and machine or man and object. Such a system can provide context-aware action recommendations without any explicit request by end users. Its prototypical realization is realized under integrated use of specially designed methods and technologies, such as the instrumentation of objects with ADOMes, process and role models, and rule recommendation procedures.