Hierarchical representation of complex systems for supporting human decision making
The work presented in this paper is devoted to intelligent on-line supervision tools. In the proposed approach, the human operator remains in the decision loop, at the highest level, and acts on the process. To help operators make decisions, process knowledge is represented with a model whose complexity can be adapted on line to the operation needs at the request of the operator. The model thus helps to focus only on the phenomena that are relevant at a given time. To give the model explanatory capacity, it is represented as a causal directed graph, and allows the representation of temporal phenomena, which is fundamental for dynamic monitoring. A hierarchical representation of the functional properties of the process is proposed. The conception of a hierarchy of causal models with a top-down analysis is discussed. Path algebra is used to construct a higher-level graph on-line at the request of the operator from the most detailed graph, while conserving the semantics of the latter. No intermediate level is defined a priori; only the highest and lowest level graphs are fixed: the others are constructed dynamically. Finally, a study of how graphs can convey information on the dynamics of the process for approximate temporal reasoning that is largely sufficient for supervision purposes is analyzed. An example of a causal graph hierarchy for a nuclear process illustrates the method. As a final point, the use of such causal graphs in advanced industrial supervision tools is considered.
supervision causal modeling operation support graph theory hierarchical modeling