IntroductionExpert systems are becoming increasingly important in industrial applications, mainly for real-time complex control systems, such as process control and network operation. In the field of real-time operation, expert systems must handle random input data such as sensor readings or alarms while processing inferences.To handle these data, it has been proposed that the inference engine and receiving process be separated. However, all input data are treated uniformly by the inference engine, so its load is still excessive [1] [2] [3][4].This paper suggests an algorithm and an inference mechanism which decrease the data processed by the inference engine in order to reduce its load.First, we clarified the scope of checked data, through data-dependency analysis. The analysis is performed by determining the sequence of inferences. Next, we describe a new inference mechanism using a multiprocess which consists of inference process (engine) and an auxiliary process (scheduler). The scheduler processes the input data, so that only events of direct relevance are passed through to the inference engine and the amount of inference processing decreases [5].In this research, we discuss inference processes for real-time control on the premise of a production system. A traditional production system consists of a knowledge base and an inference process (IE: Inference Engine) [6] [7] (Fig.l(a)). The knowledge base is a collection of rules in the form of "IF condition TIIEN action" statements. Tile inference engine consists of cycles of Pattern-Matching (MATCH), Conflict-Resolution (SELECT) and Action (ACT). The data. in local memory (WM: Working Memory) is examined by the inference engine only. For example, in diagnosing a breakdown, the cause of the breakdown is determined based on the rules and the state of the system at the point of breakdown. The Problem of Real-Time InferenceWhen we apply expert systems to the fields of realtime control, the inference engine must examine not only the data in WM but also random input data., which generally have the following characteristics [1][8]:1) Generated independently of the inference sequence 2) Values are changed frequently.If these input data are processed with the premise that changes are few, as in a traditional inference en-245
Video Hyper Media (VHM) is an environment for creating and executing interactive multimedia applications. The database of the VHM comprises multimedia contents such as still pictures, moving pictures, and sounds and scenarios that describe the behavior and dynamic relationships of compound objects (combinations of the multimedia contents). With the interactive edit function of the VHM, applications such as the on-screen subject search and a video walk-through application can be developed without any programming. As an example of an application created with the VHM, we developed a railway line information system and confirmed the effectiveness of the VHM.
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