Journal of the Japanese Society for Artificial Intelligence
Online ISSN : 2435-8614
Print ISSN : 2188-2266
Volume 7 , Issue 5
Showing 1-42 articles out of 42 articles from the selected issue
Print ISSN:0912-8085 until 2013
  • [in Japanese]
    Type: Preface
    1992 Volume 7 Issue 5 Pages 741
    Published: September 01, 1992
    Released: September 29, 2020
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  • [in Japanese]
    Type: Corner article
    1992 Volume 7 Issue 5 Pages 742-743
    Published: September 01, 1992
    Released: September 29, 2020
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  • [in Japanese]
    Type: Cover article
    1992 Volume 7 Issue 5 Pages 744-745
    Published: September 01, 1992
    Released: September 29, 2020
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  • Giyoo HATANO, Yoshio MIYAKE
    Type: Special issue
    1992 Volume 7 Issue 5 Pages 746-754
    Published: September 01, 1992
    Released: September 29, 2020
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  • Toshio INUI
    Type: Special issue
    1992 Volume 7 Issue 5 Pages 755-763
    Published: September 01, 1992
    Released: September 29, 2020
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  • Kazue HIGUCHI, Masanao TODA
    Type: Special issue
    1992 Volume 7 Issue 5 Pages 764-771
    Published: September 01, 1992
    Released: September 29, 2020
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  • Takafumi TSUCHIYA, Naomi MIYAKE
    Type: Special issue
    1992 Volume 7 Issue 5 Pages 772-778
    Published: September 01, 1992
    Released: September 29, 2020
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  • Yoshiro MIYATA
    Type: Special issue
    1992 Volume 7 Issue 5 Pages 779-785
    Published: September 01, 1992
    Released: September 29, 2020
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  • Koichi FURUKAWA
    Type: Special issue
    1992 Volume 7 Issue 5 Pages 786-795
    Published: September 01, 1992
    Released: September 29, 2020
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  • Setsuo OHSUGA
    Type: Special issue
    1992 Volume 7 Issue 5 Pages 796-809
    Published: September 01, 1992
    Released: September 29, 2020
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  • Kotaro NAKAMURA, Shigenobu KOBAYASHI
    Type: Technical paper
    1992 Volume 7 Issue 5 Pages 810-819
    Published: September 01, 1992
    Released: September 29, 2020
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    In building a large-scale knowledge system, it is quite difficult to assure the completeness and the consistency of knowledge base in advance. Therefore, it is necessary to realize a flexible framework for refining knowledge base and acquiring new knowledge through real operation of knowledge systems. In the domain of fault diagnosis of machine equipments, heuristic diagnostic knowledge, structural knowledge and failure diagnostic cases are available for diagnosis. Heuristic diagnostic knowledge has problems of incompleteness and justification and structural knowledge has a problem of inefficiency. Failure diagnostic cases can be acquired more easily than heuristic knowledge. Therefore, there exists a complementary relation among these knowledge sources. The purpose of this paper is to present a method of refinement of heuristic diagnostic knowledge based on structural knowledge. First, we presents a method of generating operational diagnostic knowledge from failure diagnostic cases, structural knowledge and measuring/testing knowledge under the framework of explanation-based learning. Then, we presents a method of incremental refinement of imperfect knowledge base based on the difference analysis between heuristic knowledge and operational knowledge. These methods have been implemented as a knowledge refinement system on ESP language. By applying it to actual diagnostic cases about cigarette making machine, its usefulness has been shown.

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  • Noboru ONO
    Type: Technical paper
    1992 Volume 7 Issue 5 Pages 820-827
    Published: September 01, 1992
    Released: September 29, 2020
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    With an experimental object-oriented database system (OODBS), the representation and retrieval of material testing data are attempted. In many cases, the matching fails between query and data objects which appear quite natural and understandable in human's viewpoint, yet missing many necessary entries in computer's viewpoint. Causes of the missing entries are discussed and methods are presented to supplement them by the default reasoning and the similar inference which embody expert knowledge. The inferences as such will play a vital role in the practical use of OODBS in many domains to make its rich expressive power and the performance of data retrieval compatible.

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  • Akira UTSUMI, Koichi HORI, Setsuo OHSUGA
    Type: Technical paper
    1992 Volume 7 Issue 5 Pages 828-836
    Published: September 01, 1992
    Released: September 29, 2020
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    In natural language processing, context dependency plays an important role. However, traditional symbol processing methods, based on static structure and lacking flexibility, make it difficult to process contextual information. Connectionist models offer an alternative method for context processing which provides dynamic structure and flexibility. For these reasons, many researchers have applied the connectionist model to problems in natural language processing, including that of context processing. For example is the DISCERN system which is based on script structure built from several neural networks. This system reads stories and answers questions about them. But this system can not process contextual information except script-based information. This paper describes our context-exploiting natural language interface system. This system uses recurrent neural network for representation of the user model. This represents the context dependent relations between a user's natural language requests and his intended application-system actions. Through learning in the recurrent network, this system can interpret the user's intended meaning of context-dependent sentences. Its context processing ability differs from that of DISCERN in many respects. For example, our system can interpret pronouns and automatically acquire the meaning of words.

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  • Masanori AKIYOSHI, Shogo NISHIDA
    Type: Technical paper
    1992 Volume 7 Issue 5 Pages 837-849
    Published: September 01, 1992
    Released: September 29, 2020
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    This paper proposes a method of qualitative simulation with association mechanism to quantitative analysis. There exist two difficult problems when predicting behaviors of physical systems with qualitative models ; one is how to construct models, and the other is how to solve ambiguity during reasoning. The proposed method here is based on two associating operations between quantitative and qualitative analysis. As for the construction of qualitative models, the method uses the dependency structure among parameters of quantitative models. Then a certain subset of whole parameters is selected by a user's viewpoints. The method extracts a reduced dependency structure for such selected parameters. Consequently qualitative models are constructed by assigning qualitative relations among selected parameters based on the reduced structure. As for pruning undesirable behaviors during reasoning, it is done by comparing qualitative behaviors with quantitative data, where we introduce converted data and interpretative time-points. Converted data and interpretative time-points are generated as follows. First, each quantitative data is converted into qualitative data from the standpoint of parameters' changing states. Second, interpretative time-points are derived from merging each duration time that is described in each of converted data. Thus the method compares the inferred qualitative behaviors with converted data along interpretative time-points. These converted data are regarded as constraint during reasoning. Third, if one inferred qualitative behavior satisfies such constraint, this method goes ahead with interpretative time-points and continues reasoning. On the other hand, if no inferred state satisfies it, it indicates modification of qualitative models or ignores some constraints by setting those constraints as qualitative states of parameters. This duration-independent characteristic of reasoning process plays an important role in applying this method to real problems. Causal explanation is also generated through the propagation process in reasoning. We apply this method to large-scale power plants and extract causal explanation.

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  • Jun MIURA, Yoshiaki SHIRAI
    Type: Technical paper
    1992 Volume 7 Issue 5 Pages 850-861
    Published: September 01, 1992
    Released: September 29, 2020
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    This paper describes a new framework of unified planning of vision and motion for a mobile robot under existence of uncertainty. In mobile robot planning in the real world, the uncertainty and the cost of visual recognition are important issues to be considered. When a robot recognizes the environment with vision, efficient views must be selected considering a trade-off between the cost of visual recognition (including the cost of motion for recognition) and the effect of information obtained by vision. We use a probabilistic model to represent uncertainties and apply statistical decision theory to constructing a unified plan of vision and motion. We propose a method of predicting sensor information and formulate the planning problem in a recurrence formula. We can justify this formulation since we use Bayes theorem to integrate sensor data from multiple views. As an example of uncertainty modeling, we construct a model of the uncertainty of stereo vision. We also analyze the planning problem using a simple example and conclude that the combination of dynamic programming (DP) and the hill-climbing method is useful for searching for optimal solutions. We apply our planner to several planning problems and show the validity of our approach. Future works are also described.

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  • Yoichiro NAKAKUKI, Yoshiyuki KOSEKI, Midori TANAKA
    Type: Technical paper
    1992 Volume 7 Issue 5 Pages 862-869
    Published: September 01, 1992
    Released: September 29, 2020
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    This paper describes an inductive learning method in probabilistic domain. It acquires an appropriate probabilistic model from a small amount of observation data. In order to derive an appropriate probabilistic model, a presumption tree with least description length is constructed. The description length of a presumption tree is defined as sum of the code length and the log-likelihood. Using the derived presumption tree, the probabilistic distribution of future events can be presumed appropriately. This capability enables improving the efficiency of certain kinds of performance systems, such as diagnostic systems, that deal with probabilistic problems.

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  • Hiroshi TSUKIMOTO
    Type: Technical paper
    1992 Volume 7 Issue 5 Pages 870-876
    Published: September 01, 1992
    Released: September 29, 2020
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    In unsupervised learning, no algorithm has been presented to induce an appropriate proposition from probabilistic data. Therefore methods such as brute-force search have been used. This paper presents an efficient algorithm to induce an appropriate proposition from probabilistic data. A brief outline of this algorithm follows. The probabilistic data is transformed into a probability vector. This probability vector is transformed into a logical vector. This logical vector is approximated by a classical logical vector. This classical logical vector is transformed into a classical logical proposition. This proposition is reduced to the minimum one. In this procedure, the most important step is the transformation from a probability vector to a logical vector. This transformation is possible, because 1) a proposition is represented as a vector in Euclidean space, and 2) the probability vector can be transformed into a logical vector by a correspondence between the logical vector and the probability vector. This algorithm has been obtained by the combination of the vector representation of logical proposition (= logical vector) and the correspondence between the logical vector and the probability vector. The vector representation of logical proposition is possible due to functional analysis of logical function. Experiments show that the propositions obtained by this algorithm fundamentally satisfy MDL criterion. This algorithm is very efficient compared with brute-force searches. We are applying this algorithm to several problems to obtain appropriate propositions or rules.

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  • Hideaki TAKEDA, Tetsuo TOMIYAMA, Hiroyuki YOSHIKAWA
    Type: Technical paper
    1992 Volume 7 Issue 5 Pages 877-887
    Published: September 01, 1992
    Released: September 29, 2020
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    This paper presents a computable design process model that is based on a logical framework. A computable design process model is useful not only to investigate designing activities but also to realize intelligent CAD (Computer-Aided-Design) systems. We propose a computable model based on a non-classical logical framework including abduction, circumscription, meta-level reasoning, and modal logic. First, we discuss design processes with a cognitive model of design processes. We point out four problems that are difficult to solve in the classical logical framework. Second, we formalize design processes in a non-classical logical framework. In this formalization, a design process is regarded as an iterative process of abduction, deduction, and circumscription. Abduction is used to obtain candidate descriptions of the design object, deduction to find out properties of the design object, and circumscription to solve inconsistency occurred in the design process. These types of inference are used for knowledge about objects ; on the other hand meta-level reasoning based on knowledge about action is used to reason out what should be done next. Furthermore we introduce data semantics to represent transitions of design states. Data semantics is a kind of modal logic which allows not only truth value t and f but also u (unknown), and a design process is interpreted as a process generating possible worlds sequentially. Then, we illustrate a design simulator based on this logical model. We discuss formalization and implementation of abductive inference and utilization of other inferences that are needed to implement a design simulator. We show that it can trace a design process in which design objects are gradually refined as the design proceeds.

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  • [in Japanese]
    Type: Corner article
    1992 Volume 7 Issue 5 Pages 888-890
    Published: September 01, 1992
    Released: September 29, 2020
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  • [in Japanese]
    Type: Other
    1992 Volume 7 Issue 5 Pages 891
    Published: September 01, 1992
    Released: September 29, 2020
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  • [in Japanese]
    Type: Other
    1992 Volume 7 Issue 5 Pages 892
    Published: September 01, 1992
    Released: September 29, 2020
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  • [in Japanese]
    Type: Corner article
    1992 Volume 7 Issue 5 Pages 893-894
    Published: September 01, 1992
    Released: September 29, 2020
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  • [in Japanese]
    Type: Corner article
    1992 Volume 7 Issue 5 Pages 894-897
    Published: September 01, 1992
    Released: September 29, 2020
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  • [in Japanese]
    Type: Corner article
    1992 Volume 7 Issue 5 Pages 898-899
    Published: September 01, 1992
    Released: September 29, 2020
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  • [in Japanese]
    Type: Corner article
    1992 Volume 7 Issue 5 Pages 900
    Published: September 01, 1992
    Released: September 29, 2020
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  • [in Japanese]
    Type: Corner article
    1992 Volume 7 Issue 5 Pages 901-902
    Published: September 01, 1992
    Released: September 29, 2020
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  • Type: Other
    1992 Volume 7 Issue 5 Pages 903
    Published: September 01, 1992
    Released: September 29, 2020
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  • Type: Activity report
    1992 Volume 7 Issue 5 Pages 904-905
    Published: September 01, 1992
    Released: September 29, 2020
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  • Type: Activity report
    1992 Volume 7 Issue 5 Pages 906-909
    Published: September 01, 1992
    Released: September 29, 2020
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  • Type: Activity report
    1992 Volume 7 Issue 5 Pages 910-925
    Published: September 01, 1992
    Released: September 29, 2020
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  • Type: Activity report
    1992 Volume 7 Issue 5 Pages 926-927
    Published: September 01, 1992
    Released: September 29, 2020
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  • Type: Activity report
    1992 Volume 7 Issue 5 Pages 928
    Published: September 01, 1992
    Released: September 29, 2020
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  • Type: Activity report
    1992 Volume 7 Issue 5 Pages 929
    Published: September 01, 1992
    Released: September 29, 2020
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  • Type: Activity report
    1992 Volume 7 Issue 5 Pages 930
    Published: September 01, 1992
    Released: September 29, 2020
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  • Type: Activity report
    1992 Volume 7 Issue 5 Pages 931-932
    Published: September 01, 1992
    Released: September 29, 2020
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  • Type: Activity report
    1992 Volume 7 Issue 5 Pages 928_2
    Published: September 01, 1992
    Released: September 29, 2020
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  • Type: Activity report
    1992 Volume 7 Issue 5 Pages b001-b018
    Published: September 01, 1992
    Released: September 29, 2020
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  • Type: Other
    1992 Volume 7 Issue 5 Pages b019-b024
    Published: September 01, 1992
    Released: September 29, 2020
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  • Type: Cover page
    1992 Volume 7 Issue 5 Pages c005
    Published: September 01, 1992
    Released: September 29, 2020
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  • Type: Cover page
    1992 Volume 7 Issue 5 Pages c005_2
    Published: September 01, 1992
    Released: September 29, 2020
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  • Type: Table of contents
    1992 Volume 7 Issue 5 Pages i005
    Published: September 01, 1992
    Released: September 29, 2020
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  • Type: Table of contents
    1992 Volume 7 Issue 5 Pages i005_2
    Published: September 01, 1992
    Released: September 29, 2020
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