A problem-solving ontology for human-centered cyber physical production systems

Abstract

Cyber physical social systems (CPSS) tend to integrate computation with physical processes as well as human and social characteristics. The fusion of cyber, physical, and socio spaces through Industry 4.0 emerges a new type of production systems known as cyber physical production systems (CPPS). CPPS enriches communications among cyber-physical-socio space in the production environment. Utilizing human-centered CPPS in smart factories (ideally) results in a mutual transition from human-machine cooperation to active collaboration, which is characterized by cyber-physical-socio interactions, knowledge exchange and reciprocal learning. The shift from data workers or producers to problem-solver is, therefore, triggered to both humans and CPPS, respectively. Hence, their job roles and responsibilities cannot be independently defined. This paper approaches the collaboration of human and CPPS in problem-solving from the angle of complementarity whereby “human competences” and “CPPS autonomy” together derive supplementary capability and reciprocal learning. In this research, “Problem” is an umbrella term that refers to both categories of “human-CPPS task” (i.e. a specific piece of work required to be done) and “failure event” (i.e. a state of difficulty that needs to be resolved). A holistic ontological framework is proposed, entitled PSP Ontology (Problem, Solution, Problem-Solver Ontology), which represents the logical relations between the three super-concepts of “Problem Profile”, “Problem-Solver Profile”, and “Solution Profile”. Related entities are formalized by introducing (i) contingency vector, (ii) vector of competence and autonomy, and (iii) solution maturity index, respectively. PSP Ontology is utilized for semantic representation of the super-concepts and reasoning out the competence questions, i.e. in which situation and under which conditions human and/or CPPS is dominant or eligible to solve a problem (to accomplish a given task and/or to detect or eliminate a failure), which is qualitatively exemplified in the use-case of maintenance 4.0.