Journal of the Japanese Society for Artificial Intelligence
Online ISSN : 2435-8614
Print ISSN : 2188-2266
Volume 3 , Issue 5
Showing 1-35 articles out of 35 articles from the selected issue
Print ISSN:0912-8085 until 2013
  • [in Japanese]
    Type: Preface
    1988 Volume 3 Issue 5 Pages 531
    Published: September 20, 1988
    Released: September 29, 2020
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  • [in Japanese]
    Type: Corner article
    1988 Volume 3 Issue 5 Pages 532
    Published: September 20, 1988
    Released: September 29, 2020
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  • [in Japanese], [in Japanese], [in Japanese], [in Japanese], [in Japane ...
    Type: Article
    1988 Volume 3 Issue 5 Pages 533-543
    Published: September 20, 1988
    Released: September 29, 2020
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  • [in Japanese], [in Japanese], [in Japanese], [in Japanese], [in Japane ...
    Type: Article
    1988 Volume 3 Issue 5 Pages 544-551
    Published: September 20, 1988
    Released: September 29, 2020
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  • Mitsuru ISHIZUKA
    Type: Corner article
    1988 Volume 3 Issue 5 Pages 552-562
    Published: September 20, 1988
    Released: September 29, 2020
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  • Katsumi NITTA
    Type: Corner article
    1988 Volume 3 Issue 5 Pages 563-571
    Published: September 20, 1988
    Released: September 29, 2020
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  • Hitoshio MATSUBARA, Katsuhiko SAKAUE, Naokazu YOKOYA, Kazuhiko YAMAMOT ...
    Type: Corner article
    1988 Volume 3 Issue 5 Pages 572-580
    Published: September 20, 1988
    Released: September 29, 2020
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  • Kiyoshi AKAMA
    Type: Technical paper
    1988 Volume 3 Issue 5 Pages 581-589
    Published: September 20, 1988
    Released: September 29, 2020
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    HYPOSE is an extended prolog with logical constraint descriptions. The declarative interpretation of the inference of HYPOSE is to find a subset D of the given hypothesis set⊿, such that the union of fact set F and D implies the given query Q under the restriction that the union of F and D does not imply the given constraint C. Procedurally, the constraint works as a demon that watches the execution of the query Q so as not to violate the given constraint C. HYPOSE increases the expressive power of constraints, since we can write constraints in the same logical way as we write programs in prolog. It allows us to implement a very efficient inference with constraints, since the consttaint demon can work incrementally. It supplies a natural framework for dealing logically with hypotheses and changing knowledge base, while standard prolog does not give a good logical treatment for them.

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  • Norihiro ABE, Tomohiro ISHIKAWA, Saburo TSUJI
    Type: Technical paper
    1988 Volume 3 Issue 5 Pages 590-598
    Published: September 20, 1988
    Released: September 29, 2020
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    It is not easy for us to instruct robots using robot language like AL because its task specification are not familiar to us. If the programming is permitted in natural language, novices will be able to access any robot systems. This system is given as input a set of English sentences in an instruction manual of mechanical assembly, and it regards them as a rough plan which specifiesrelations among mechanical parts to be assembled. As the specification is not precise, the system must refine it by referring to 3-D geometric models of objects and a world model that reflects the current state of assembling environment. Ambiguity in language specification and the assumption that common sense knowledge on assembling operations is available to us makes this refinement process difficult. At first, sentences are analyzed using DCG parser in PROLOG and the results are converted into frame structures. Instructions in the given sentence are converted into OPERATION frame. At this stage, several slots cannot be filled because sentences do not mention conditions in detail. They must be determined referring to object models and a world model. Even if candidates of slot fillers have been found, it is not clear whether the obtained operation will be possible or not. For this purpose, the system must simulate the operation and select an alternative by retrying the wrong decision. Concerning the simulation, it is not always true that a terminal condition of an operation is stated in sentences, and the system should take care of a termination of the operation. For example, START operation in a sentence will be transformed into INSERT operation, but no specification on its completion may be stated there. Then the system must assume the condition, but the system cannot judge the correctness of the assumption withoutestimating the rest of the sentences, so the system tries to find it using backtracking. As such, the system cannot tell whether the assembled object is the same one to be assembled or not. Consequently an estimation is needed for confirming the correctness of the assembled object. As the system is not given any explicit goal statement on the assembly, we have given to it a heuristic estimation.

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  • Azuma OHUCHI, Masahito KURIHARA, Ikuo KAJI
    Type: Technical paper
    1988 Volume 3 Issue 5 Pages 599-606
    Published: September 20, 1988
    Released: September 29, 2020
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    The bottleneck in the process of building expert systems is retrieving the appropriate problem solving knowledge from the human expert. Method of developing an understanding of complex situations from systems engineering based on Interpretive Structural Modeling(ISM)is applied to this process. A theoretical framework of the lnteractive Knowledge Structural Modeling(IKSM) is described which interactively developing the knowledge structures by computer asistence. IKSM process is devided into two main phases, embedding phase and analytic phase. The embedding phase is to elicit the knowledge needed to solve analysis problems. Three implication rules are derived and applied to effectively execute the embedding phase. The analytic phase then arranges the knowledge to a hierarchy structure. This process extracts equivalence class, partial ordering, covering, and connected parts from the knowledge. The result is presented by some figure easily understood by the human expert.

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  • Hitoshi ISAHARA, Shun ISHIZAKI
    Type: Technical paper
    1988 Volume 3 Issue 5 Pages 607-616
    Published: September 20, 1988
    Released: September 29, 2020
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    A natural language understanding system is described which extracts syntactical and semantic information from Japanese newspaper articles. The understanding system parses and understands the input sentences and constructs an intermediate representation in CRS(Contextual Representation Structure)which is independent from the specific languages. The system employes a syntactic analyzer and semantic analyzer. These analyzers operate in a serially lntegrated fashion. The syntactic analyzer(context free grammar parser)analyzes the input sentences and obtains the parsing trees. Since the phrase order in a clause is grammatically unrestricted in Japanese sentences, and since, unlike in English, positional information regarding phrases is usually no help in establishing their relation, the Japanese analyzer is unable to determine phrase relations. So, this Japanese analyzer extracts an rough sketch of a structure of the sentences and the semantic relations between words are determined in the next semantic process. The syntactic structure adopts the right recursive tree structure to represent this Japanese unrestricted construction. This tree structure contains the all ambiguity in the syntactic analysis. From an analysis of these, in turn, the semantic analyzer obtains word-level semantic structures. The semantic analyzer constructs a semantic structure for each phrase. Each word meaning shares a suitable position in the hierarchy of concepts. In this system, a word meaning written in the lexical entry plays an important role in semantic analysis. The interaction between the word meanings enables the check if a word can be embedded in the slot in the other word. If the check is satisfied, the analyzer embeds the word in a suitable slot in the other word. The modifying ralations inside noun phrases and the case realtions among verbs and noun phrases are determined in the word-level semantic structure.

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  • Hayato OHWADA, Fumio MIZOGUCHI, Yoshiaki KITAZAWA
    Type: Technical paper
    1988 Volume 3 Issue 5 Pages 617-626
    Published: September 20, 1988
    Released: September 29, 2020
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    This paper describes a method for developing diagnostic systems through qualitative simulation. The method can be viewed as a practical approach to knowledge base construction using current qualitative simulation techniques. Unlike the previous experiential approaches, diagnostic rules are derived from a structural model, which is described as qualitative parameters and constraints among them. The rule generation procedure is achieved by finding the difference between the behavior of normal state and that of abnormal state. The system called QR/P is designed and implemented in logic programming languages to test on a medical domain. An experiment on intraocular pressure mechanism generated 60 rules applied to glaucoma diagnostic system directly. The present method has novel features : First, QR/P predicts the behavior of an abnormal state by a slight change of the initial condition representing the normal state. Second, conditional probabilities are given by ambiguities due to qualitativeness. Third, QR/P computes subjective probabilities associated with rules by using ordinaly probability theory. These capabilities contribute to the connection between model-based and experiential approach to developing knowledge based systems.

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  • Taro KAMIOKA, Yuichiro ANZAI
    Type: Technical paper
    1988 Volume 3 Issue 5 Pages 627-638
    Published: September 20, 1988
    Released: September 29, 2020
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    The sentence analysis algorithm based on DCG has a weak point that the analysis itself fails if the analyzed sentence contains unknown words, namely the words not stored in the dictionary. But it is not realistic that every word in the analyzed sentence should be stored in the dictionary. We have tried to take advantage of the hypothesis generation mechanism for analyzing the sentence contalning unknown words. In this paper, we make use of a subset of ICR(b-ICR), which is an interpreter for explanation of given goals with hypothesis generation mechanism based on the SLD-resolution, as a meta-interpreter to DCG top-down algorithm, so that the analysis should not fail even if the analyzed sentence contains unknown words. The b-ICR checks up each of failing subgoal atom in SLD-resolution and if the atom can be matched to the specified pattern(permitted pattern), b-ICR regards it as hypothesis atom and continues the interpretation without failing. If the syntactic and semantic attributes of words is designed to permitted pattern and the interpreter of b-ICR is applied to the meta-interpreter of DCG top-down algorithm, b-ICR generates the knowledge about unknown words as hypothesis set.

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  • [in Japanese]
    Type: Other
    1988 Volume 3 Issue 5 Pages 639-640
    Published: September 20, 1988
    Released: September 29, 2020
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  • [in Japanese]
    Type: Corner article
    1988 Volume 3 Issue 5 Pages 641
    Published: September 20, 1988
    Released: September 29, 2020
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  • [in Japanese]
    Type: Corner article
    1988 Volume 3 Issue 5 Pages 642
    Published: September 20, 1988
    Released: September 29, 2020
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  • [in Japanese]
    Type: Corner article
    1988 Volume 3 Issue 5 Pages 643
    Published: September 20, 1988
    Released: September 29, 2020
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  • [in Japanese]
    Type: Corner article
    1988 Volume 3 Issue 5 Pages 644-645
    Published: September 20, 1988
    Released: September 29, 2020
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  • Type: Other
    1988 Volume 3 Issue 5 Pages 646-647
    Published: September 20, 1988
    Released: September 29, 2020
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  • Type: Activity report
    1988 Volume 3 Issue 5 Pages 648-652
    Published: September 20, 1988
    Released: September 29, 2020
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  • Type: Activity report
    1988 Volume 3 Issue 5 Pages 653-656
    Published: September 20, 1988
    Released: September 29, 2020
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  • Type: Activity report
    1988 Volume 3 Issue 5 Pages 657-661
    Published: September 20, 1988
    Released: September 29, 2020
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  • Type: Activity report
    1988 Volume 3 Issue 5 Pages 662-667
    Published: September 20, 1988
    Released: September 29, 2020
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  • Type: Activity report
    1988 Volume 3 Issue 5 Pages 668-669
    Published: September 20, 1988
    Released: September 29, 2020
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  • Type: Activity report
    1988 Volume 3 Issue 5 Pages 670-671
    Published: September 20, 1988
    Released: September 29, 2020
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  • Type: Activity report
    1988 Volume 3 Issue 5 Pages 672-677
    Published: September 20, 1988
    Released: September 29, 2020
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  • Type: Activity report
    1988 Volume 3 Issue 5 Pages 678
    Published: September 20, 1988
    Released: September 29, 2020
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  • Type: Activity report
    1988 Volume 3 Issue 5 Pages 679
    Published: September 20, 1988
    Released: September 29, 2020
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  • Type: Activity report
    1988 Volume 3 Issue 5 Pages 680
    Published: September 20, 1988
    Released: September 29, 2020
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  • Type: Activity report
    1988 Volume 3 Issue 5 Pages 681
    Published: September 20, 1988
    Released: September 29, 2020
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  • Type: Activity report
    1988 Volume 3 Issue 5 Pages 682-683
    Published: September 20, 1988
    Released: September 29, 2020
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  • Type: Activity report
    1988 Volume 3 Issue 5 Pages b001-b006
    Published: September 20, 1988
    Released: September 29, 2020
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  • Type: Cover page
    1988 Volume 3 Issue 5 Pages c005
    Published: September 20, 1988
    Released: September 29, 2020
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  • Type: Cover page
    1988 Volume 3 Issue 5 Pages c005_2
    Published: September 20, 1988
    Released: September 29, 2020
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  • Type: Table of contents
    1988 Volume 3 Issue 5 Pages i005
    Published: September 20, 1988
    Released: September 29, 2020
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