Journal of the Japanese Society for Artificial Intelligence
Online ISSN : 2435-8614
Print ISSN : 2188-2266
Volume 2 , Issue 3
Showing 1-42 articles out of 42 articles from the selected issue
Print ISSN:0912-8085 until 2013
  • [in Japanese]
    Type: Corner article
    1987 Volume 2 Issue 3 Pages 257
    Published: September 01, 1987
    Released: September 29, 2020
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  • [in Japanese]
    Type: Corner article
    1987 Volume 2 Issue 3 Pages 258
    Published: September 01, 1987
    Released: September 29, 2020
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  • [in Japanese]
    Type: Corner article
    1987 Volume 2 Issue 3 Pages 259
    Published: September 01, 1987
    Released: September 29, 2020
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  • [in Japanese]
    Type: Corner article
    1987 Volume 2 Issue 3 Pages 260
    Published: September 01, 1987
    Released: September 29, 2020
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  • [in Japanese]
    Type: Corner article
    1987 Volume 2 Issue 3 Pages 261
    Published: September 01, 1987
    Released: September 29, 2020
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  • [in Japanese]
    Type: Corner article
    1987 Volume 2 Issue 3 Pages 262
    Published: September 01, 1987
    Released: September 29, 2020
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  • [in Japanese]
    Type: Corner article
    1987 Volume 2 Issue 3 Pages 263
    Published: September 01, 1987
    Released: September 29, 2020
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  • Type: Corner article
    1987 Volume 2 Issue 3 Pages 264
    Published: September 01, 1987
    Released: September 29, 2020
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  • Hitoshi MATSUBARA, Kazuhiko YAMAMOTO
    Type: Corner article
    1987 Volume 2 Issue 3 Pages 266-272
    Published: September 01, 1987
    Released: September 29, 2020
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  • Hiroyuki YOSHIKAWA
    Type: Corner article
    1987 Volume 2 Issue 3 Pages 273-279
    Published: September 01, 1987
    Released: September 29, 2020
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  • [in Japanese]
    Type: Cover article
    1987 Volume 2 Issue 3 Pages 280-281
    Published: September 01, 1987
    Released: September 29, 2020
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  • Tatsuo UNEMI
    Type: Special issue
    1987 Volume 2 Issue 3 Pages 282-288
    Published: September 01, 1987
    Released: September 29, 2020
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  • Jun-ichi TOYODA, Kuniaki UEHARA
    Type: Special issue
    1987 Volume 2 Issue 3 Pages 289-298
    Published: September 01, 1987
    Released: September 29, 2020
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  • Setsuo ARIKAWA
    Type: Special issue
    1987 Volume 2 Issue 3 Pages 299-306
    Published: September 01, 1987
    Released: September 29, 2020
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  • Tomoharu MOHRI
    Type: Special issue
    1987 Volume 2 Issue 3 Pages 307-315
    Published: September 01, 1987
    Released: September 29, 2020
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  • Eiki CHIGIRA, Masakazu KOBAYASHI
    Type: Special issue
    1987 Volume 2 Issue 3 Pages 316-323
    Published: September 01, 1987
    Released: September 29, 2020
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  • Kuniaki UEHARA, Takashi KAKIUCHI, Jun-ichi TOYODA
    Type: Technical paper
    1987 Volume 2 Issue 3 Pages 324-332
    Published: September 01, 1987
    Released: September 29, 2020
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    This paper describes an integrated parser for text understanding. In the integrated parser, processes of event-driven and expectation-driven understanding occur as an integral part of the parsing process. That is, mechanisms of event assimilation, event search, and 'top-down' inference are incorporated into a single module. The mechanisms of event assimilation and event search are used to find coherent relations between successive portions of a text (i.e., event-driven understanding). The 'top-down' inference mechanism using common sense knowledge is utilized to expect what is coming next (i.e., expectation-driven understanding). The common sense knowledge for expectation-driven understanding, called hierarchical event structure, is a property inheritance net that organizes a hierarchy of events. The hierarchical event structure is encoded explicitly into computational entities called actors which were proposed by Hewitt. When the integrated parser inputs the word associated with a particular event in the hierarchical event structure, it sends a message to the corresponding actor. The actor expects what kind of events are likely to occur in the text. Events extracted from the text are stored in an actor 'EVENT'. The knowledge for event-driven understanding, which is associated with lexical entries, is utilized to detect the coherent relation between events. Each lexical entry is also encoded into an actor, whereas the knowledge for event-driven understanding is encoded implicitly within the procedural information of the actor. When the actor 'EVENT' receives the message followed by the procedural information, it extracts the coherent relation between the stored events and the input sentence. The contextual information contained in the text is, thus, available to resolve anaphora and to select meanings of ambiguous words during the parsing of a sentence.

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  • Takahira YAMAGUCHI, Riichiro MIZOGUCHI, Naoki TAOKA, Hiroshi KODAKA, Y ...
    Type: Technical paper
    1987 Volume 2 Issue 3 Pages 333-340
    Published: September 01, 1987
    Released: September 29, 2020
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    Most expert systems built by the current generation tools use shallow knowledge (heuristics), which is a collection of "pattern & xrarr; action" associative rules without a deep understanding of the domain. Although the shallow knowledge approach contributed to the success of the first generation expert systems, it turns out to cause several problems. And it is suggested by several researchers that deep knowledge is the key idea to resolve these problems. Deep knowledge can be seen as the textbook knowledge which human experts have about the domain of the expertise. There is, however, little discussion on comparison of the cost for building shallow knowledge base with that for deep knowledge base. Namely, there is no idea about what kinds of deep knowledge an expert system should have and about how they are used in deep reasoning, which generates rules from various kinds of deep knowledge. Thus there is still some distance between the new generation tools based on deep knowledge and the current ones. This paper discusses Knowledge Compiler (KC), which is a system to perform deep reasoning in "machinery domain" aiming at developing a next generation tool. In regard with deep knowledge, we attach importance to the cost for acquiring it from the viewpoint of tools. For examples, since detailed causal network cannot be represented without domain-dependent form, it costs too much for coming into the tools and so we do not adopt such deep knowledge. Thus the authors have investigated deep knowledge as knowledge source for generating shallow knowledge, and obtained the following four kinds of knowledge: (1) Device World (DW) (2) Physical World (PW) (3) Control World (CW) (4) Interpretation World (IW) DW includes "intention of a machine designer" besides design information incorporating shape, measurements and configuration of components. PW has many kinds of physical principle and the condition on which it is applied. And it is useful in order to envision what phenomenon is caused in the machine. CW includes controllability of a physical parameter, durability of a component and observability of a symptom and then is used for rule generation process control. IW contains knowledge for mapping one physical state to a trouble-hypothesis and/or symptom. On the other hand, KC generates diagnosis rules from the above-mentioned four kinds of deep knowledge and then consists of Deep Forward Reasoning (DFR), Deep Backward Reasoning (DBR) and Control Modules. DFR tries to make propagation, starting from a given elementary symptom to get trouble-hypothesis relevant to it. DBR tries to make propagation, starting from the trouble-hypothesis obtained from DFR in order to get other symptoms and to accomplish the incomplete rule. CW is used to generate rules in order of "importance". Thus rule generation process is controlled by deep knowledge in CW. In this paper, diagnosing overheat problem of a car engine will be taken as an example. However, techniques developed here are common to all machineries and KC will be a kernel in developing the next generation tool.

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  • Kiyoshi AKAMA
    Type: Technical paper
    1987 Volume 2 Issue 3 Pages 341-349
    Published: September 01, 1987
    Released: September 29, 2020
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    LS/1 is a domain independent inductive learning system, which repeats Question-Response-Answer (Q - R - A) interactions with a teacher, and learns the structure of the relation between questions and answers. Each message (Q, R or A) is a sequence of words. LS/1 can be applied to the task of learning English-Japanese translation without any changes in the algorithm of the system by using a training sequence which consists of English sentences as questions and their Japanese translations as answers. In a learning experiment on an easy English-Japanese translation sequence, LS/1 effectively acquires knowledge for translation, which includes word-to-word correspondence and grammatical knowledge. The important point we stress here is that the successful result in learning of translation has been attained by an inductive learning system whose learning algorithm and knowledge representation system are constructed independently of concepts and terms related to translation, and whose initial knowledge involves no domain specific, syntactic or semantic knowledge. The knowledge representation system of LS/1 is called a label net, which has an expressive power to represent relational data bases, concept hierarchy and certainty factors of rules. We define basic concepts of label nets, such as deductivity, explainability and applicability. The response generation procedure of LS/1 utilizes a best first search algorithm to find the most plausible solution sentence. Label nets are closely related to the axiom sets of Horn clauses. Concepts and algorithms similar to the ones mentioned above can also be discussed in the Horn clause framework. The label net of LS/1 is more appropriate than Horn clauses when we construct learning systems which can discover unknown structures in sentence data. The learning algorithm of LS/1 includes generalization and merging algorithms of rules, generalization by identification of two predicates, and changing certainty factors in rules. Such learning algorithms are controlled by some conservative heuristics in order to avoid combinatorial explosions. This paper suggests that LS/1 is an effective research framework for the learning of translation as well as for the learning in other question-answering processes, and that research of translation learning in our framework is very useful in extending the domain independent theory of inductive learning.

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  • Guidong HAN, Setsuo OHSUGA
    Type: Technical paper
    1987 Volume 2 Issue 3 Pages 350-358
    Published: September 01, 1987
    Released: September 29, 2020
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    CAD systems currently in use, developed on the basis of the traditional computing technology, can help man to a very limited extent. These systems can not deal with concepts, ideas, and experience which play important roles performing design under the very dynamic environment. We attempt here to introduce knowledge processing technology into CAD in order that computer can cooperate more effectively with man in the design process than it has been possible on the basis of the traditional computing technology. Design is a process in which, first, the object model is built tentatively and, then, the sequence of operations such as the model analysis, the model evaluations, and the model modifications is repeated to the point where the given requirements are satisfied by the object model. It is quite necessary for an advanced CAD to automatize this sequence of operations. The analysis is performed usually based on the suitable analysis model for obtaining solution. This model is represented in very different way from the object model but, as the matter of course, these models must be equivalent each other from the analysis point of view. That is, the former must be generated from the latter. Let us call the former the function model. Thus in order to automatize the operations above, it is necessary to automatize the generation of the function model from the object model and its evaluation. In this paper, we discuss first the characteristics of a general design process, and the role of a function model in the process. Then we introduce briefly a knowledge-and-model representation language called Multi-Layer Logic, and a knowledge based system named KAUS (Knowledge Acquisition and Utilization System) designed on this basis. Then we show, in an example of applying this system to an internal combustion engine design, the manner of generating and evaluating a function model by means of knowledge base and inference.

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  • Noboru BABAGUCHI, Kenji MURAKAMI, Tsunehiro AIBARA
    Type: Technical paper
    1987 Volume 2 Issue 3 Pages 359-366
    Published: September 01, 1987
    Released: September 29, 2020
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    Classical logics such as propositional logic and first-order predicate logic are called, in general, monotonic logic. On the other hand, logics which can invalidate old conclusions when adding some new knowledge are called nonmonotonic logic. Nonmonotonic reasoning based on such logics is of great importance for the realization of commonsense reasoning or incomplete knowledge reasoning in knowledge information processing system. One of the interesting approaches to the nonmonotonic reasoning is D. McDermott and J. Doyle's nonmonotonic logic. However, there exist some problems in their logic. In order to avoid the problems, R. C. Moore then reconstructed an alternative nonmonotonic logic, called autoepistemic logic. At present, autoepistemic logic could be regarded as one of the most promising formalizations of nonmonotonic reasoning. Autoepistemic logic is on the basis of the notion of belief and is intended to model the beliefs of an ideally rational agent reflecting upon his own beliefs. This paper consists mainly of the following three topics. The first is to survey Moore's propositional autoepistemic logic. The second is to redefine the stable expansions which are the possible sets of the agent's total beliefs for a given set of premises, in terms of the fixed point relation with respect to a certain operator. The third is to give a decision procedure by using a tableau method, whether or not a formula appears in all stable expansions, and to prove the correctness of its procedure.

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  • Hideyuki NAKASHIMA
    Type: Technical paper
    1987 Volume 2 Issue 3 Pages 367-374
    Published: September 01, 1987
    Released: September 29, 2020
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    Uranus is a programming system designed for knowledge representation. The multiple world mechanism of Uranus makes it possible to represent conceptual space and time sequence. In this paper, we examine commonsense reasoning with this mechanism. By commonsense reasoning, we mean reasoning with rules which have exceptions. The results must fit our intuition. To do so requires ordering among rules. Non-monotonic logic is too weak for this purpose. We show that the multiple world mechanism of Uranus provides the natural ordering among rules, and thus it is suitable for commonsense reasoning.

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  • Hidehiko KITA, Toshiki SAKABE, Yasuyoshi INAGAKI
    Type: Research note
    1987 Volume 2 Issue 3 Pages 375-378
    Published: September 01, 1987
    Released: September 29, 2020
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    This paper makes it clear what is to be proved for the program verification in the framework of algebraic semantics of programming languages. First, we give an algebraic specification method of programming languages which is covenient to considering program verification, where the meaning of a program is considered as a function from inputs to outputs. Next we consider the intended function of a program as an abstract data type and specify it algebraically. Then we give an algebraic definition of the correctness of programs based on these frameworks

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  • [in Japanese]
    Type: Other
    1987 Volume 2 Issue 3 Pages 379
    Published: September 01, 1987
    Released: September 29, 2020
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  • [in Japanese]
    Type: Other
    1987 Volume 2 Issue 3 Pages 380
    Published: September 01, 1987
    Released: September 29, 2020
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  • [in Japanese]
    Type: Corner article
    1987 Volume 2 Issue 3 Pages 381
    Published: September 01, 1987
    Released: September 29, 2020
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  • [in Japanese]
    Type: Corner article
    1987 Volume 2 Issue 3 Pages 382
    Published: September 01, 1987
    Released: September 29, 2020
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  • [in Japanese]
    Type: Corner article
    1987 Volume 2 Issue 3 Pages 383
    Published: September 01, 1987
    Released: September 29, 2020
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  • Type: Other
    1987 Volume 2 Issue 3 Pages 384
    Published: September 01, 1987
    Released: September 29, 2020
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  • Type: Activity report
    1987 Volume 2 Issue 3 Pages 385-387
    Published: September 01, 1987
    Released: September 29, 2020
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  • Type: Activity report
    1987 Volume 2 Issue 3 Pages 388-392
    Published: September 01, 1987
    Released: September 29, 2020
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  • Type: Activity report
    1987 Volume 2 Issue 3 Pages 393-395
    Published: September 01, 1987
    Released: September 29, 2020
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  • Type: Activity report
    1987 Volume 2 Issue 3 Pages 396-397
    Published: September 01, 1987
    Released: September 29, 2020
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  • Type: Activity report
    1987 Volume 2 Issue 3 Pages 398-403
    Published: September 01, 1987
    Released: September 29, 2020
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  • Type: Activity report
    1987 Volume 2 Issue 3 Pages 404-405
    Published: September 01, 1987
    Released: September 29, 2020
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  • Type: Activity report
    1987 Volume 2 Issue 3 Pages 406-407
    Published: September 01, 1987
    Released: September 29, 2020
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  • Type: Activity report
    1987 Volume 2 Issue 3 Pages 408-409
    Published: September 01, 1987
    Released: September 29, 2020
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  • Type: Corner article
    1987 Volume 2 Issue 3 Pages 264_2-265
    Published: September 01, 1987
    Released: September 29, 2020
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  • Type: Activity report
    1987 Volume 2 Issue 3 Pages b001-b002
    Published: September 01, 1987
    Released: September 29, 2020
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  • Type: Cover page
    1987 Volume 2 Issue 3 Pages c003
    Published: September 01, 1987
    Released: September 29, 2020
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  • Type: Cover page
    1987 Volume 2 Issue 3 Pages c003_2
    Published: September 01, 1987
    Released: September 29, 2020
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  • Type: Table of contents
    1987 Volume 2 Issue 3 Pages i003
    Published: September 01, 1987
    Released: September 29, 2020
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