Journal of the Japanese Society for Artificial Intelligence
Online ISSN : 2435-8614
Print ISSN : 2188-2266
Volume 5 , Issue 2
Showing 1-25 articles out of 25 articles from the selected issue
Print ISSN:0912-8085 until 2013
  • [in Japanese]
    Type: Preface
    1990 Volume 5 Issue 2 Pages 145
    Published: March 01, 1990
    Released: September 29, 2020
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  • Toru ISHIDA
    Type: Corner article
    1990 Volume 5 Issue 2 Pages 146-153
    Published: March 01, 1990
    Released: September 29, 2020
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  • [in Japanese], [in Japanese]
    Type: Cover article
    1990 Volume 5 Issue 2 Pages 154
    Published: March 01, 1990
    Released: September 29, 2020
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  • Tetsuji ABE, Yukio AKIYAMA, Yutaka MITSUNAGA
    Type: Technical paper
    1990 Volume 5 Issue 2 Pages 155-162
    Published: March 01, 1990
    Released: September 29, 2020
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    Nowadays, a huge-scale telecommunication network has been constructed in Japan, and high level maintenance technology is required to keep the system reliable. Especially, since the subscriber network has intricate structure and many outside equipments, the maintenance of it is difficult, and a expert system is expected to make the reliability higher. In this paper, a subscriber network fault location expert system developed as one of the maintenance system is described. By investigating the fault location problem and characteristics of knowledge which we can collect, it is found that the principle knowledge of this problem is past fault facts of each equipment of the system. Thus, a reasoning method that utilize knowledge of past fault facts effectively is proposed. The reasoning method infers the failure possibility of every equipments using a concept of failure physics. It is proved that fundamental failure factors must be derived from the past fault fact to avoid the overlap or contradiction of knowledge. Then, a method to obtain those factors is also proposed. It is shown that the failure factor can easily derived using this method. A prototype system is also developed. Because the reasoning method is based on the failure physics, the reasoning results have objectivity. And the system also has an advantage that the fault facts knowledge can be simply represented by the frame. Consequently, it is confirmed that the system has enough capacity for practical application.

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  • Kensei TSUCHIDA, Yukari Cecilia AKAO, Keiichi IMAI
    Type: Technical paper
    1990 Volume 5 Issue 2 Pages 163-172
    Published: March 01, 1990
    Released: September 29, 2020
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    The Debug Expert System (DBES) is a system that debugs and diagnoses switching systems software. The system has been developed together with the experts and the users who debug switching systems software, with the aims of satisfying the requirements of both and of building a system that comports itself in the same way as humans. For this, new concepts such as initial information and unknown treatment have been introduced. Initial information are facts that do not change while debugging some specific field of the debugging environment and that can be initialized before the inference starts. Unknown treatment deals with information that the consultant does not know or whose meaning the consultant does not understand. Through the development of the DBES, a model of knowledge necessary for debugging switching systems has been created with the help of experts. It explains how final users of the DBES can use this knowledge (that is, it explains the new concepts, such as initial information and unknown treatment) and how the experts' knowledge is organized. In addition, this model has played an important part in unifying the terminology used by experts and the knowledge engineers who developed the DBES. This enhances the DBES so that experts can increment and maintain the knowledge by themselves. The DBES has been evaluated through experimental use. The DBES editor that orients and aids expert's work in transferring their knowledge to the DBES and that allows the modification of knowledge data in real time has been constructed for actual use based on the model and evaluations described above.

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  • Masaaki UENO, Yosihiko TAMURA
    Type: Technical paper
    1990 Volume 5 Issue 2 Pages 173-183
    Published: March 01, 1990
    Released: September 29, 2020
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    This paper proposes a quantitative-model-based approach to the knowledge representation for the process diagnosis system to support plant operation. Observed operators' processing may justifiably recommend the usage of the quantitative process model. The process model is basically made with the knowledge in chemical engineering, and practically modified with time to follow the varying state of the plant process. On the basis of the process model, the representative term "balance-measure" is newly introduced. The terms in each model expression equivalently explain the balanced situation in the model, and are availed to evaluate the process situation. Plant feature prior to the present is described in terms of the balance-measure term from the data already gathered and compiled as the models to be referred in the form of the set of the term. The present process state is also described in the term. After comparison between the present state and the references, estimated difference between them suggests the faulty changes in the plant process. The process state variable is defined to compose of three elements ; the theoretical value in the model, the indicated measure, and the deviation between the prior two. The diagnosis is performed by inferring the value of the deviation in each model.

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  • Shin-ichi MORISHITA, Masayuki NUMAO, Yoshio TOZAWA
    Type: Technical paper
    1990 Volume 5 Issue 2 Pages 184-193
    Published: March 01, 1990
    Released: September 29, 2020
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    This paper proposes cooperative scheduling to solve complex process-scheduling problems in actual plants. Factory management calls for complex decisions and analysis of production processes, incoming orders, cost accounting, etc., based on a wide variety of information on manufacturing systems in today's frequently changing technical and marketing environments. Cooperative scheduling provides a full-screen-oriented interface which displays a graphic diagram of a schedule and allows the user to modify it by direct manipulation. When the user edits a schedule, he can just concentrate on modifying its global structure without paying attentions to some constraints, such as machine conflicts and domain specific constraints. After a user's modification, constraints are automatically satisfied by the system very quickly, while preserving the global structure of the schedule. Some constraints are solved algorithmically, and others are satisfied by using rules. Rule-base capabilities, therefore, are also essential in cooperative scheduling. After constraints are satisfied, the user revise the schedule and modify it if some portions cannot be acceptable. The user repeats these steps until he obtains a feasible schedule. To justify our approach, a system called Scheplan has been developed and tested in an actual plant for an operational use. According to experimental reports, the daily scheduling time is greatly reduced compared with the time done by a limited number of experts. Furthermore, the quality of the daily schedule is much improved.

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  • Fumimaro SEKINE, Takashi TAKENAKA, Seigo HONDA, Hiroyuki EHARA, Shiget ...
    Type: Technical paper
    1990 Volume 5 Issue 2 Pages 194-202
    Published: March 01, 1990
    Released: September 29, 2020
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    A knowledge based system was developed to make production line schedules. Many scheduling system were developed with knowledge engineering approaches. However these systems used only human expert's heuristics, which were deterministic problem solving knowledge, that lead to only feasible solutions. In order to get better schedules, we analysed human expert's problem solving process, and developed an integrated method, which includes both human expert's heuristics and a quantitative search guided with a cost evaluation function. For the actual use of the system, it consists of not only scheduler, but also consists of schedule modifier and simulator. The scheduler makes cost efficient schedules automatically. A decision maker can modify the schedules from global point of view by using the modifier. At that time, he can also know cost efficiency of the modified schedules simultaneously, which is evaluated by the simulator. Aided by these functions, "very good" real executable schedules can be got.

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  • Hirokazu TAKI, Kazuhiro TSUBAKI
    Type: Technical paper
    1990 Volume 5 Issue 2 Pages 203-212
    Published: March 01, 1990
    Released: September 29, 2020
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    This paper describes Expert Model, a knowledge representation for knowledge acquisition and Pre-post method, a knowledge acquisition method based on the expert model. The expert model consists of operations which represent small tasks in human experts' problem solving processes. We have prepared generic operations for effective model building. We assumed that there is some representation technique in real knowledge bases because knowledge engineers build them, although the production rules are in a very simple and general form. We have obtained seven types of generic operations by analyzing real knowledge bases which are written for diagnosis problems in production rules. The pre-post method has two interview strategies. It stimulates a human expert to remember by ask what operations are done before and after an operation. It extracts knowledge about operations efficiently, according to the operation type. This paper also introduces a knowledge acquisition support system, EPSILON/One, based on the expert model and the pre-post method. It has been implemented on PSI (Personal Sequential Inference machine) in ESP (Extended Self-contained Prolog). It consists of a human interface for interview, a knowledge elicitation module, a knowledge refinement module, and a knowledge translation and inference module. The interface supports graphic multi-windows and a mouse. The knowledge elicitation module extracts knowledge in the form of the expert model by the pre-post method. The knowledge refinement module supports to detect a lack of knowledge. The knowledge translation and inference module translates the expert model into knowledge in the form of ESP language and supports to evaluate the knowledge in inference processes.

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  • Kiyoshi TOHDOH, Shunji MATSUMOTO, Tomoaki SATO
    Type: Technical paper
    1990 Volume 5 Issue 2 Pages 213-219
    Published: March 01, 1990
    Released: September 29, 2020
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    We are developing an intelligent development environment for knowledge systems based on ES/SDEM (Software Development Engineering Methodology for Expert Systems). The environment provides not only development tools such as editors or debugger, but also knowledge base called Knowledge Ware (KW) which can be used on different applications. KW is written using an object-oriented language on Common Lisp, and will provide the following advantages. ・User can build up expert systems rapidly using differential programming technique. ・Adding the parts made for an expert system to KW, it is easy to use the same parts for developing another expert systems. In this paper, the approach for building KW and the function of KW will be shown.

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  • Naoki KANAI, Yoshio TOZAWA
    Type: Technical paper
    1990 Volume 5 Issue 2 Pages 220-230
    Published: March 01, 1990
    Released: September 29, 2020
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    Planning is one of the most important tasks in business. Planners have conceptual models of business and knowledge ; these models consist of (1) factors that are represented as numerical values by abstracting certain elements of business, and (2) the constraints that these factors must satisfy. The goal of planning is to decide the values of the factors that satisfy all constraints. However, it is difficult to define all constraints and knowledge rigidly, because planners are not explicitly Conscious of them all. There are some that they never perceive until they are confronted with particular situations. Therefore, it is very difficult to develop systems that generate plans automatically. By representing explicitly definable parts of conceptual models and knowledge in the systems, we can develop expert systems to generate plans that satisfy only defined constraints. We refer to these systems as planning support expert systems (PSESs). PSESs cooperate with the planners in making plans. Planners are the best people to develop PSESs. To do so, they require planning environments in which they can develop the systems without programming, because they are not usually experts in this. All that should be required of them is to define their conceptual models and knowledge. Therefore, planning environments should provide mechanisms to generate plans that satisfy all defined constraints, and user interfaces that allow interaction between the systems and planners. Inspire is a planning environment based on the above idea. Numerical factors, equality constraints among factors, and dependencies among them are defined as models in the system, and other constraints and knowledge about how to satisfy them are defined as rules in knowledge bases. Inspire provides an algorithmic constraint satisfier to satisfy all equality constraints automatically, and uses defined rules to satisfy other constraints. It also provides a spreadsheet-type interface. As a result, planners can develop PSESs in Inspire without programming.

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  • [in Japanese]
    Type: Other
    1990 Volume 5 Issue 2 Pages 231
    Published: March 01, 1990
    Released: September 29, 2020
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  • [in Japanese]
    Type: Corner article
    1990 Volume 5 Issue 2 Pages 232-234
    Published: March 01, 1990
    Released: September 29, 2020
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    Download PDF (376K)
  • [in Japanese], [in Japanese]
    Type: Corner article
    1990 Volume 5 Issue 2 Pages 235-236
    Published: March 01, 1990
    Released: September 29, 2020
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  • [in Japanese]
    Type: Corner article
    1990 Volume 5 Issue 2 Pages 237-238
    Published: March 01, 1990
    Released: September 29, 2020
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    Download PDF (170K)
  • [in Japanese]
    Type: Corner article
    1990 Volume 5 Issue 2 Pages 239-240
    Published: March 01, 1990
    Released: September 29, 2020
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  • Type: Other
    1990 Volume 5 Issue 2 Pages 241
    Published: March 01, 1990
    Released: September 29, 2020
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  • Type: Activity report
    1990 Volume 5 Issue 2 Pages 242-246
    Published: March 01, 1990
    Released: September 29, 2020
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  • Type: Activity report
    1990 Volume 5 Issue 2 Pages 247-249
    Published: March 01, 1990
    Released: September 29, 2020
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    Download PDF (219K)
  • Type: Activity report
    1990 Volume 5 Issue 2 Pages 250-253
    Published: March 01, 1990
    Released: September 29, 2020
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  • Type: Activity report
    1990 Volume 5 Issue 2 Pages 254-255
    Published: March 01, 1990
    Released: September 29, 2020
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    Download PDF (185K)
  • Type: Activity report
    1990 Volume 5 Issue 2 Pages b001-b006
    Published: March 01, 1990
    Released: September 29, 2020
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  • Type: Cover page
    1990 Volume 5 Issue 2 Pages c002
    Published: March 01, 1990
    Released: September 29, 2020
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  • Type: Cover page
    1990 Volume 5 Issue 2 Pages c002_2
    Published: March 01, 1990
    Released: September 29, 2020
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  • Type: Table of contents
    1990 Volume 5 Issue 2 Pages i002
    Published: March 01, 1990
    Released: September 29, 2020
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