The evolution from the intelligent network to the advanced intelligent network (AIN) is described. The concept behind AIN and its architecture are introduced. A Bellcore product for the AIN, the SPACE system, is illustrated. The authors discuss the user-friendly programming language, service creation platform, multiple-services application, and reliable node platform of the SPACE system.>

Human information processing and task selection procedures in a dynamic multitask supervisory control environment are discussed. The results of a joint experimental and analytic program were assimilated into a normative dynamic-decision model for predicting human task-selection performance. To this end a general multitask experimental paradigm has been developed, wherein tasks of different value, time requirement, and deadline compete for a human's attention. Via this framework, the effects of various task related variables on human-decision processes have been studied empirically. Conceptually the normative dynamic-decision model (DDM) is an outgrowth of the well-known optimal control modeling technology as applied to multitask situations. Thus the analytic framework of the DDM is rooted in modern control, estimation, and semiMarkov decision-process theories. In order to validate the model via comparison with experimental results, several time history and scalar measures of performance similarity are proposed. Excellent model-data agreement is obtained for all the experimental conditions studied.

Present researh has sought to expand our understanding of human information processing and control behavior In target tracking tasks. Specifically, It has focused on the problem of quantifying the human's "internal" model that charaterizes his perception of short-term target motion, and on the development of concomitant adaptive schemes for generating esimates of target velodty and acceleation using these models. A combined experimental and analytic program has studied simulated target trking performance as modified by short periods (~1 s) of target blanking. The blankin occur at pseudorandom times during a run. During the blanking period, human operator performance is governed almost entirely by his internal model representation of the target motion. Ensemble data from these blanking experiments have been used to suitably refine the optimal control model, including the target submodel. The resulting model represents the state of the art with regard to human operator modeing In dynamic antiaircraft-artillery (AAA) systems.

The effects of gust disturbances and the pilot's participation mode in the control task on his work-load level and failure detection performance during a simulated low visibility landing approach are examined. We found that the participation mode had a strong effect on the pilot's work load, the induced work load being lowest when the pilot acted as a monitoring element during a coupled approach and highest when the pilot was an active element in the control loop. The effects of differential work load and participation mode on failure detection were separated. The participation mode was shown to have a dominant effect on the failure detection performance, with a failure in a monitored (coupled) axis being detected significantly faster than a comparable failure in a manually controlled axis.