The ordinary differential equations (ODEs) are used as a mathematical method for the sake of modeling a complicated nonlinear system. This approach is well-known to be useful for the practical application, e.g., bioinformatics, chemical reaction models, controlling theory etc. In this paper, we propose a new evolutionary method by which to make inference of a system of ODEs. To explore the search space more effectively in the course of evolution, the right-hand sides of ODEs are inferred by Genetic Programming (GP) and the least mean square (LMS) method is used along with the ordinary GP. We apply our method to several target tasks and empirically show how successfully GP infers the systems of ODEs. We also describe how our approach is extended to solve the inference of a differential equation system including transdential functions.

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A knowledge acquisition method Ripple Down Rules (RDR) can directly acquire and encode knowledge from human experts. It is an incremental acquisition method and each new piece of knowledge is added as an exception to the existing knowledge base. Past researches on RDR method assume that the problem domain is stable. This is not the case in reality, especially when an environment changes. Things change over time. This paper proposes an adaptive Ripple Down Rules method based on the Minimum Description Length Principle aiming at knowledge acquisition in a dynamically changing environment. We consider the change in the correspondence between attribute-values and class labels as a typical change in the environment. When such a change occurs, some pieces of knowledge previously acquired become worthless, and the existence of such knowledge may hinder acquisition of new knowledge. In our approach knowledge deletion is carried out as well as knowledge acquisition so that useless knowledge is properly discarded to ensure efficient knowledge acquisition while maintaining the prediction accuracy for future data. Furthermore, pruning is incorporated into the incremental knowledge acquisition in RDR to improve the prediction accuracy of the constructed knowledge base. Experiments were conducted by simulating the change in the correspondence between attribute-values and class labels using the datasets in UCI repository. The results are encouraging.

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In this paper, we propose a completion procedure (called MKB_po) for term rewriting systems. Based on the existing procedure MKB which works with multiple reduction orderings and the ATMS nodes, the MKB_po improves its performance by restricting the class of reduction orderings to precedence-based path orderings, representing them by logical functions in which a logical variable x_fg represents the precedence f > g. By using BDD (binary decision diagrams) as a representation of logical functions, the procedure can be implemented efficiently. This makes it possible to save the number of quasi-parallel processes effectively and suppress the rapid increase in the amount of computation time asymptotically.

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Previous commentary systems generate commentaries only from the viewpoint of a commentator. However there are various viewpoints for comments, and such different viewpoints invoke various comments. The amount of information about a situation may differ between the viewpoints, and the understandings of the situation may also differ between them. In this paper, we propose a method to generate commentaries automatically so that users can easily understand situations by taking into account the different understandings of the situations between viewpoints. Our method is composed of two parts. The first is generation of comment candidates about the current situation, unexpected actions, intentions of players by using a game tree. The second is comment selection which chooses comments related to the prior one so that listeners can compare the situations from different viewpoints. Based on our approach, we implemented an experimental system that generates commentaries on mahjong games. We discuss the output of the system.

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In this paper, a new machine-learning method, called Dual-Schemata model, is presented. Dual-Schemata model is a kind of self-organizational machine learning methods for an autonomous robot interacting with an unknown dynamical environment. This is based on Piaget's Schema model, that is a classical psychological model to explain memory and cognitive development of human beings. Our Dual-Schemata model is developed as a computational model of Piaget's Schema model, especially focusing on sensori-motor developing period. This developmental process is characterized by a couple of two mutually-interacting dynamics; one is a dynamics formed by assimilation and accommodation, and the other dynamics is formed by equilibration and differentiation. By these dynamics schema system enables an agent to act well in a real world. This schema's differentiation process corresponds to a symbol formation process occurring within an autonomous agent when it interacts with an unknown, dynamically changing environment. Experiment results obtained from an autonomous facial robot in which our model is embedded are presented; an autonomous facial robot becomes able to chase a ball moving in various ways without any rewards nor teaching signals from outside. Moreover, emergence of concepts on the target movements within a robot is shown and discussed in terms of fuzzy logics on set-subset inclusive relationships.

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This paper proposes a new method for selecting fundamental vocabulary. We are presently constructing the Fundamental Vocabulary Knowledge-base of Japanese that contains integrated information on syntax, semantics and pragmatics, for the purposes of advanced natural language processing. This database mainly consists of a lexicon and a treebank: Lexeed (a Japanese Semantic Lexicon) and the Hinoki Treebank. Fundamental vocabulary selection is the first step in the construction of Lexeed. The vocabulary should include sufficient words to describe general concepts for self-expandability, and should not be prohibitively large to construct and maintain. There are two conventional methods for selecting fundamental vocabulary. The first is intuition-based selection by experts. This is the traditional method for making dictionaries. A weak point of this method is that the selection strongly depends on personal intuition. The second is corpus-based selection. This method is superior in objectivity to intuition-based selection, however, it is difficult to compile a sufficiently balanced corpora. We propose a psychologically-motivated selection method that adopts word familiarity as the selection criterion. Word familiarity is a rating that represents the familiarity of a word as a real number ranging from 1 (least familiar) to 7 (most familiar). We determined the word familiarity ratings statistically based on psychological experiments over 32 subjects. We selected about 30,000 words as the fundamental vocabulary, based on a minimum word familiarity threshold of 5. We also evaluated the vocabulary by comparing its word coverage with conventional intuition-based and corpus-based selection over dictionary definition sentences and novels, and demonstrated the superior coverage of our lexicon. Based on this, we conclude that the proposed method is superior to conventional methods for fundamental vocabulary selection.

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Weblogs (blogs) are now thought of as a potentially useful information source. Although the definition of blogs is not necessarily definite, it is generally understood that they are personal web pages authored by a single individual and made up of a sequence of dated entries of the author's thoughts, that are arranged chronologically. In Japan, since long before blog software became available, people have written `diaries' on the web. These web diaries are quite similar to blogs in their content, and people still write them without any blog software. As we will show, hand-edited blogs are quite numerous in Japan, though most people now think of blogs as pages usually published using one of the variants of public-domain blog software. Therefore, it is quite difficult to exhaustively collect Japanese blogs, i.e., collect blogs made with blog software and web diaries written as normal web pages. With this as the motivation for our work, we present a system that tries to automatically collect and monitor Japanese blog collections that include not only ones made with blog software but also ones written as normal web pages. Our approach is based on extraction of date expressions and analysis of HTML documents, to avoid having to depend on specific blog software, RSS, or the ping server.

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With the rapid advance of information technology, we are able to easily and quickly obtain a great deal of information on almost any topic. One method by which to managing such large amounts of information is to utilize catalogs which organize information within concept hierarchies. However, the concept hierarchy for each catalog is different because one concept hierarchy is not sufficient for all purposes. In the present paper, we address the problem of integrating multiple catalogs for ease of use. The primary problem lies in finding a suitable category in a catalog for each information instance in another catalog. Three approaches can be used to solve this problem: ontology integration approach, instance classification approach and category alignment approach based on categorization similarity. The main idea of this paper is a multiple strategy approach to combine the instance classification approach and the category alignment approach. In order to evaluate the proposed method, we conducted experiments using two actual Internet directories, Yahoo! and Google. The obtained results show that the proposed method improves upon or is competitive with the integration method based only on category alignment or instance classification. Therefore, the proposed catalog integration method is shown to be an effective combination of the instance classification approach and the category alignment approach.

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The application of kernel methods to citation analysis is explored. We show that a family of kernels on graphs provides a unified perspective on the three bibliometric measures that have been discussed independently: relatedness between documents, global importance of individual documents, and importance of documents relative to one or more (root) documents (relative importance). The framework provided by the kernels establishes relative importance as an intermediate between relatedness and global importance, in which the degree of `relativity,' or the bias between relatedness and importance, is naturally controlled by a parameter characterizing individual kernels in the family.

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In this paper, we propose an inter-personal information sharing model among individuals based on personalized recommendations. In the proposed model, we define an information resource as shared between people when both of them consider it important --- not merely when they both possess it. In other words, the model defines the importance of information resources based on personalized recommendations from identifiable acquaintances.
The proposed method is based on a collaborative filtering system that focuses on evaluations from identifiable acquaintances. It utilizes both user evaluations for documents and their contents. In other words, each user profile is represented as a matrix of credibility to the other users' evaluations on each domain of interests. We extended the content-based collaborative filtering method to distinguish other users to whom the documents should be recommended.
We also applied a concept-based vector space model to represent the domain of interests instead of the previous method which represented them by a term-based vector space model. We introduce a personalized concept-base compiled from each user's information repository to improve the information retrieval in the user's environment. Furthermore, the concept-spaces change from user to user since they reflect the personalities of the users. Because of different concept-spaces, the similarity between a document and a user's interest varies for each user. As a result, a user receives recommendations from other users who have different view points, achieving inter-personal information sharing based on personalized recommendations.
This paper also describes an experimental simulation of our information sharing model. In our laboratory, five participants accumulated a personal repository of e-mails and web pages from which they built their own concept-base. Then we estimated the user profiles according to personalized concept-bases and sets of documents which others evaluated. We simulated inter-personal recommendation based on the user profiles and evaluated the performance of the recommendation method by comparing the recommended documents to the result of the content-based collaborative filtering.

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The World Wide Web (WWW) allows a person to access a great amount of data provided by a wide variety of entities. However, the content varies widely in expression. This makes it difficult to browse many pages effectively, even if the contents of the pages are quite similar. This study is the first step toward the reduction of such variety of WWW contents. The method proposed in this paper enables us to easily obtain information about similar objects scattered over the WWW. We focus on the tables contained in the WWW pages and propose a method to integrate them according to the category of objects presented in each table. The table integrated in a uniform format enables us to easily compare the objects of different locations and styles of expressions.

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Since there are a lot of digital resources on WWW,

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This paper presents a mathematical model for decision making processes where the knowledge for the decision is constructed automatically from subjective information on the Internet. This mathematical model enables us to know the required degree of accuracy of knowledge acquisition for constructing decision support systems using two technologies: automated knowledge acquisition from information on the Internet and automated reasoning about the acquired knowledge. The model consists of three elements: knowledge source, which is a set of subjective information on the Internet, knowledge acquisition, which acquires knowledge base within a computer from the knowledge source, and decision rule, which chooses a set of alternatives by using the knowledge base. One of the important features of this model is that the model contains not only decision making processes but also knowledge acquisition processes. This feature enables to analyze the decision processes with the sufficiency of knowledge sources and the accuracy of knowledge acquisition methods.
Based on the model, decision processes by which the knowledge source and the knowledge base lead to the same choices are given and the required degree of accuracy of knowledge acquisition is quantified as required accuracy value. In order to show the way to utilize the value for designing the decision support systems, the value is calculated by using some examples of knowledge sources and decision rules. This paper also describes the computational complexity of the required accuracy value calculation and shows a computation principle for reducing the complexity to the polynomial order of the size of knowledge sources.

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As the internet becomes the basic resource of information, not only texts but images retrieval systems have been appeared. However, many of those supply only a list of images, so we have to seek the expecting images one by one. Although, image labeling is one of the solutions of such a problem, various words are labeled to an image if the words are extracted from only one Web page. Therefore, this paper proposes an image clustering system that labels images by words related to a search keyword. This relationships are measured by Web pages in WWW. By the experimental results, users were enabled to find the intended images more faster than the ordinal image search system.

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