The study in the field of scientific discovery from data has been directed to the discovery of plausible law equations representing the first principles underlying objective systems. In this paper, a novel principle and an algorithm to predictively discover new scientific law equation formulae consisting of newly given quantities are proposed based on the candidate law equations governing the other quantities under current observation. The first principle-based scientific law equation formulae must follow some mathematical admissibility and consistency. These conditions enable efficient reasoning of the law equation formulae in the prediction process. The soundness and the reproducibility of the equation prediction by this approach have been tested through numerical simulations of physical examples, and, moreover, its practicality has been confirmed through a real socio-psychological analysis. The approach can discover a set of scientific law equations representing common first principles under different set of quantities, and enables to capture general scietific features of the objective system under analysis.

The human-centered automation concept claims two general requirements on situation awareness (SA) of human operators. One is the necessity of keeping the SA of an artifact, and the other is keeping the SA of automation. Nevertheless, the fundamental methods or techniques, especially quantitative ones, are not mature enough to be used as rational tools for human-machine design. An important approach for establishing the method must be identification of a proper "level of automation", where ten levels are proposed by T.B. Sheridan (1999). A goal of the research presented is to establish a theoretical framework for evaluating automation levels from a viewpoint of operator's situation awareness in real domains. A method using discrete event simulation is developed, and applied to a particular artifact: a nuclear power plant.