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Minoru ASADA
Article type: Preface
2000 Volume 15 Issue 1 Pages
1
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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Tatsuya AKUTSU, Minoru ASOGAWA, Akihiko KONAGAYA
Article type: Cover article
2000 Volume 15 Issue 1 Pages
2
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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Tatsuya AKUTSU, Minoru ASOGAWA, Akihiko KONAGAYA
Article type: Special issue
2000 Volume 15 Issue 1 Pages
3-10
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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Hiroaki KITANO
Article type: Special issue
2000 Volume 15 Issue 1 Pages
11-20
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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Masaru TOMITA, Kenta HASHIMOTO, Koich TAKAHASHI, Yuri MATSUZAKI, Ryo M ...
Article type: Special issue
2000 Volume 15 Issue 1 Pages
21-26
Published: January 01, 2000
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YUTAKA AKIYAMA
Article type: Special issue
2000 Volume 15 Issue 1 Pages
27-34
Published: January 01, 2000
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Kiyoshi ASAI
Article type: Special issue
2000 Volume 15 Issue 1 Pages
35-42
Published: January 01, 2000
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Masami HAGIYA
Article type: Special issue
2000 Volume 15 Issue 1 Pages
43-50
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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Yuji KOHARA
Article type: Special issue
2000 Volume 15 Issue 1 Pages
51-55
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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Takao TERANO
Article type: Cover article
2000 Volume 15 Issue 1 Pages
56
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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Yukio OHSAWA
Article type: Special issue
2000 Volume 15 Issue 1 Pages
57-60
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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Focusing on areas with the highest risk of future earthquakes is important, especially if the technique is simple enough to be adopted to existing electronic data bases. By trenching survey, i.e., digging the land surface for observing underground evidences of past earthquakes, we can approximately estimate long we have before the next earthquake - an inefficient and expensive approach. An intelligent data-mining for focusing attention to risky active faults will help in achieving a less expensive and more efficient predictions of earthquakes. From the viewpoint of artificial intelligence, this is an essential challenge of having a simple computing algorithm for solving a complex problem as estimating the risks at faults.
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Kazunori FUJIMOTO, Hideto KAZAWA, Hiroshi SATO, Akinori ABE, Kazumitsu ...
Article type: Special issue
2000 Volume 15 Issue 1 Pages
61-64
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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Decision Support for Internet Users, Called DSIU, is an area of research for providing decision support for Internet users by using information on the Internet. DSIU aims to provide decision support with logicalexplanation taking account of user's preference. By using information extraction techniques, DSIU handles the names of various substances, e.g., electronic products, persons, and places, and so on, and constructs the explanations in terms of their properties. This paper describes the DSIU particularly form a viewpoint of realizing the DSIU and giving contributions to society in the near future. The information of DSIU is available at http://www.kecl.ntt.co.jo/DSIU/.
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Kaou HIRAMATSU
Article type: Special issue
2000 Volume 15 Issue 1 Pages
65-68
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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The purpose of this research is to integrate WWW information and sensory data into a Digital City. The key parts of our project are geographical homepage link (GeoLink) database, WWW map interfaces using city metaphor and information retrieving based on real time sensory data. Our project goal are to create a new information space which changes dynamically and continuously and to support community networking.
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Shintaro AZECHI, Nobuhiko FUJIHARA, Kaoru SUMI, Takashi HIRATA, Hiroyu ...
Article type: Special issue
2000 Volume 15 Issue 1 Pages
69-73
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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We propose novel communication medium, a public opinion channel (POC). Its main function is to exchange and to share opinions in a community. It gathers messages from community members, integrates them into a meaningful story, and broadcasts the story to community members. Because the story is summarized, community members can avoid perceiving extreme amount of information. It aids community members so survey opinions in a community and to make a public discussion. Community members send new information about the story to POC again. By this process, information circulates through the community. The POC can be regarded as the next challenge in information and communication technologies. Three issues are discussed: 1) Does the POC facilitate communication in the community? 2) How does it compose stories based on messages from community members? 3) What social effects does it produce?
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Takao TERANO
Article type: Special issue
2000 Volume 15 Issue 1 Pages
74-77
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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The performance of recent smart IC cards is equal to the one of micro-computer kits about twenty years ago. Using the functions of smart IC cards, this paper proposes yet another mobile computing system for community computing, which aims at supporting personal information management, information service, and dynamic collaborative filtering. The main features of the system are (1) the agents, or fairies in a card are small, cheap, and easy-to-use; (2) they are fully distributed among ubiquitous computing environments with and/or without computer networks; and (3) holder-centered information controlling mechanisms. The application candidates include travel navigation, collaborative education, and electronic commerce. This paper discusses the system architecture, feasibility of applications, and future issues.
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Katsumi NITTA
Article type: Cover article
2000 Volume 15 Issue 1 Pages
78
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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[in Japanese]
Article type: Special issue
2000 Volume 15 Issue 1 Pages
79
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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[in Japanese]
Article type: Special issue
2000 Volume 15 Issue 1 Pages
80
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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Shi YAN
Article type: Special issue
2000 Volume 15 Issue 1 Pages
81
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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Wenwei YU
Article type: Special issue
2000 Volume 15 Issue 1 Pages
82
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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[in Japanese]
Article type: Special issue
2000 Volume 15 Issue 1 Pages
83
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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Xiaolong ZHANG
Article type: Special issue
2000 Volume 15 Issue 1 Pages
84
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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[in Japanese]
Article type: Special issue
2000 Volume 15 Issue 1 Pages
85
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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[in Japanese]
Article type: Special issue
2000 Volume 15 Issue 1 Pages
86
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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[in Japanese]
Article type: Special issue
2000 Volume 15 Issue 1 Pages
87
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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[in Japanese]
Article type: Special issue
2000 Volume 15 Issue 1 Pages
88
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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[in Japanese]
Article type: Special issue
2000 Volume 15 Issue 1 Pages
89
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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[in Japanese]
Article type: Special issue
2000 Volume 15 Issue 1 Pages
90
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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[in Japanese]
Article type: Special issue
2000 Volume 15 Issue 1 Pages
91
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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[in Japanese]
Article type: Special issue
2000 Volume 15 Issue 1 Pages
92
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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[in Japanese]
Article type: Special issue
2000 Volume 15 Issue 1 Pages
93
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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[in Japanese]
Article type: Special issue
2000 Volume 15 Issue 1 Pages
94
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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[in Japanese]
Article type: Special issue
2000 Volume 15 Issue 1 Pages
95
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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[in Japanese]
Article type: Special issue
2000 Volume 15 Issue 1 Pages
96
Published: January 01, 2000
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[in Japanese]
Article type: Special issue
2000 Volume 15 Issue 1 Pages
97
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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[in Japanese]
Article type: Special issue
2000 Volume 15 Issue 1 Pages
98
Published: January 01, 2000
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[in Japanese]
Article type: Special issue
2000 Volume 15 Issue 1 Pages
99
Published: January 01, 2000
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[in Japanese]
Article type: Special issue
2000 Volume 15 Issue 1 Pages
100
Published: January 01, 2000
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[in Japanese]
Article type: Special issue
2000 Volume 15 Issue 1 Pages
101
Published: January 01, 2000
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Kang-Hyun JO
Article type: Special issue
2000 Volume 15 Issue 1 Pages
102
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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[in Japanese]
Article type: Special issue
2000 Volume 15 Issue 1 Pages
103
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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[in Japanese]
Article type: Special issue
2000 Volume 15 Issue 1 Pages
104
Published: January 01, 2000
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[in Japanese]
Article type: Special issue
2000 Volume 15 Issue 1 Pages
105
Published: January 01, 2000
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Inhyuk MOON
Article type: Special issue
2000 Volume 15 Issue 1 Pages
106
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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[in Japanese]
Article type: Special issue
2000 Volume 15 Issue 1 Pages
107
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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[in Japanese]
Article type: Special issue
2000 Volume 15 Issue 1 Pages
108
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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Tamito KAJIYAMA, Hideaki ITO, Saburou IIDA, Teruo FUKUMURA
Article type: Technical paper
2000 Volume 15 Issue 1 Pages
109-118
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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This paper presents an overview of the TRICS system which discovers a conceptual structure of an existing relational database, and its mechanisms for eliciting the conceptual structure of the database from its relational instances. In order to represent the conceptual structure of the database, it is necessary to make conceptual objects and relationships between them clear. Conceptual objects and their relationships are obtained based on functional and inclusion dependencies. These dependences are inductively inferred by analyzing a collection of relational instances. Functional dependencies are used for making conceptual objects clear, while inclusion dependencies for relating these conceptual objects to each other. A conceptual object is specified with a relation which is in third normal form (3NF). If an existing relation is not in 3NF, the relation is decomposed into some 3NF relations. Each conceptual object is transformed into a frame. There are three types of relationships between two frames, they are is-a, is-part-of is-associated-with. A hierarchical structure of frames is constructed based on is-a links. Part-whole relationships are represented in terms of is-part-of links. And, relationships for relating associative objects are represented in is-associated-with links. By eliciting conceptual objects and their relationships, and describing them in terms of frames, a structural representation of a existing relational database is inductively obtained. It will improve the transparency and understandability of the database's structure. Such a structural representation is also useful for integrating the existing database into a knowledgebase system, since the knowledgebase system has to retain a description of the data structure of the database.
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Fumiaki IMANARI, Hitoshi OGAWA
Article type: Technical paper
2000 Volume 15 Issue 1 Pages
119-128
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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This paper proposes a new approach to the path planning problem with dynamic change of movement time of paths. For the dynamic environmental change, we try to cope with it to modify a temporal plan dynamically without using any estimated functions. In our proposed method, firstly a temporal path plan is generated based on the initial state of the problem. Secondly, every one move to the next node, according to the observation of environmental change, the temporal plan is dynamically modified in order to reach the goal in shorter movement time, if necessary. In order to find shortcut-paths, we introduce a multi-agent model, in which agents search for them by diffusing messages to neighboring agents. Since where and whom agents send messages is determined based on the present status of the problem, modifications are executed appropriately and locally, and admissible solutions are obtained efficiently. We examine the relation between effective behavior and a variety of settings of parameters for the problem. As a result of the experiment, we show that our proposed method is more useful for the problem in which the average of delay of movement time is longer and the existence probability of delay of movement time is smaller.
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Nobuo SUEMATSU, Akira HAYASHI
Article type: Technical paper
2000 Volume 15 Issue 1 Pages
129-136
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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The methods which use ensembles of classifiers to improve accuracy in supervised learning have received much attention. Especially, much empirical and theoretical work has been made on AdaBoost. Given a base learner and a set of training examples, AdaBoost maintains of training examples and constructs and ensemble of classifiers by applying the base learner to the weighted training set iteratively while updating the weights. The empirical studies have reported that AdaBoost improves accuracy dramatically in many domains. Boosting was proposed and had been analyzed in the context of concept learning (i.e. Boolean-valued function estimation). However, many real classification problems will be intrinsically stochastic and they will not fit into the framework in which AdaBoost has been analyzed. Nevertheless, little attention has been paid for Boosting in the context of probabilistic model learning so far. In this paper, we propose an algorithm which is inspired by the Boosting to learn probabilistic of AdaBoost and our algorithm. We also investigate the relation between our algorithm and AdaBoost. It shows that our algorithm closely related to AdaBoost and they coincide under some restricted settings. These results shed light on an extension of the Boosting to the probabilistic model learning.
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Minoru HARADA, Yoshihide NOMURA, Takahiro MIZUNO
Article type: Technical paper
2000 Volume 15 Issue 1 Pages
137-146
Published: January 01, 2000
Released on J-STAGE: September 29, 2020
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A lot of rule-based systems are used today. Its reliability mainly depends on the correctness of the rules extracted from the expert. However, it is impossible for any expert to prepare before hand all the rules applicable to any situation. Hence, if an incorrect execution result comes out, it is usual to correct the rule base by manual operation. The purpose of this paper is to develop system THERES (THeory REvision System) which automates this correction of rules. The rule base which THERES revises is a Horn clause set described with prolog. The predicates which compose the rule base are assumed to be divided into the correct predicates (called basic predicates) which compose he rule base are assumed to be divided into the correct predicates (called basic predicates) as the target of maintenance, whose body section is described by basic predicates (single rule decidable rule base). After the rule base system is applied to a new problem, only by dividing the results into the positive cases and the negative cases and also by pointing out the new positive case which were not derived by the rule base but are thought to be correct, THERES automatically revises the rule base so as to solve this problem correctly. Concretely, THERES first finds a wrong rule. Then by using progol, THERES acquires the rules called discrimination rules, which can separate the positive cases and the negative cases covered by the wrong rule. Finally, THERES adds the body of the discrimination rule to the body of the wrong rule in the form of conjunction. Such a correction method looks like to add the unconsidered restriction to an exception to the existing rule. Therefore, it is more practical than to replace the existing rules by the rules acquired by inductive learning from the cases.
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