Origin of language is discussed. Language is a media to represent, process and transfer concepts. Before language has been made human being has only biological system to preserve their lives like the other animals. How was concepts made and language originated from there? In this paper a possibility of language having been originated is discussed.

In order to discover new, surprising, interesting patterns hidden in data, peculiarity oriented mining and multidatabase mining are required. In the paper, we introduce peculiarity rules as a new class of rules, which can be discovered from a relatively low number of peculiar data by searching the relevance among the peculiar data. We give a formal interpretation and comparison of three classes of rules: association rules, exception rules, and peculiarity rules, as well as describe how to mine more interesting peculiarity rules in multiple databases.

Problems arising in the near future may grow larger and more complex beyond the human capability for managing them and it is worrying that this can cause trouble for human society. The only possible way to solve this problem is to make computers more intelligent to back up the weak points of human beings. At least, it is necessary to provide a method for capturing and recording all human decisions in problem-solving. This view leads one to an idea of a computer-led interactive system such that the computer becomes able to manage problem-solving process and makes records of individual decisions made by a human being. Autonomy, generality and practicality are the major requirements for such computer-led interactive systems. New information technology to meet this condition is discussed. It is very different from the conventional computer technology in many aspects. The major topics are: a model-based software architecture for future information systems, a modeling scheme to accept and represent a wide area of problems, a method for externalizing human ideas and of representing it as a model, a method for solving problems represented in the form of a model and so on. It outlines a part of the research work being promoted by the author's group under the sponsorship of the Science and Technology Agency of the Japanese Government.

How to increase both autonomy and versatility of a knowledge discovery system is a core problem and a crucial aspect of KDD (Knowledge Discovery and Data Mining). Within the framework of the KDD process and the GLS (Global Learning Scheme) system recently proposed by us, this paper describes a way of increasing both autonomy and versatility of a KDD system by dynamically organizing KDD processes. In our approach, the KDD process is modeled as an organized society of KDD agents with multiple levels. We propose an ontology to describe KDD agents, in the style of OOER (Object Oriented Entity Relationship) data model. Based on this ontology of KDD agents, we apply several AI planning techniques, which are implemented as a meta-agent, so that we might (1) solve the most difficult problem in a multiagent KDD system: how to automatically choose appropriate KDD techniques (KDD agents) to achieve a particular discovery goal in a particular application domain; (2) tackle the complexity of KDD process; and (3) support evolution of KDD data, knowledge and process. The GLS system, as a multistrategy and multiagent KDD system based on the methodology, increases both autonomy and versatility.

The 21st century is the age of the Internet and the World Wide Web. The Web revolutionizes the way we gather, process, and use information. At the same time, it also redefines the meanings and processes of business, commerce, marketing, finance, publishing, education, research, development, as well as other aspects of our daily life. The revolution is just beginning. Although individual Web based information systems are constantly being deployed, advanced issues and techniques for developing and for benefiting from Web intelligence still remain to be systematically studied. The article defines a new research field, namely Web Intelligence (WI) by giving a complete picture of WI related topics for systematic study on advanced Web technology and developing Web based intelligent information systems. Roughly speaking, WI exploits AI and advanced information technology on the Web and Internet. It is the key and the most urgent research field of IT for business intelligence.

The authors have built and evaluated several systems to assist creative concept formation by professional engineers and scientists. Through experiments, it has become clear that a system that can reveal different viewpoints automatically is strongly needed by many users to support their creative activities. We are all surrounded by an almost infinite amount of information. If we can elicit different viewpoints from large information sources, we can arrive at new understandings that could not have been possible through discussions with other persons alone. This paper presents a system that automatically elicits and visualizes different viewpoints of authors concerning certain topics from a text database of journal and conference papers. Users can review different viewpoints of a topic of interest by looking at a space configured by the system. Through interaction with the system, users are expected to build their own concepts creatively. The effect of promoting creative concept formation and other benefits, such as enhanced information retrieval and knowledge sharing, have been validated by several experiments.

A document retrieval system for researchers in the field of science and technology is described. The main feature of the system is the visualization of the semantic relations between documents and interactive operation to it. The system has a test database composed of a large number of journal and conference papers. It elicits keywords from each paper by an automatic indexing algorithm and then visualizes the relations between the papers and their keywords in a metric space by a statistical method. We carried out several experiments. Through them we have confirmed that our system is effective because it can assist the users in their query formation and modification while they use it. Our system can also assist the users in their creative research because they can find the relations between the papers and their keywords which they haven't noticed so far. In the experiment on effectiveness of retrieval we have confirmed that our system can realize high recall and precision ratio.

Research issues in the domain of AI for design can be organized in three categories: decision making, representation and knowledge handling. In the area of knowledge handling, this paper addresses issues concerning the management of design experience to guide a priori the generation of candidate solutions. The approach is based on keeping the trace of a previous design experience as a hierarchical knowledge base. A level in the hierarchy can be viewed as a level of granularity of the description of the design process. A general framework for defining a partial order function between the granularity levels in the knowledge bases of design expertise is proposed. It is then possible to compute the sets of the elements belonging to smaller granularity levels, which are linked to any component of the hierarchy. Thus, it makes it possible to compute the level in the hierarchy that can be reused without modification for the design of a new product. Computation of the appropriate level is mainly based on matching the data corresponding to the new requirements with these sets. The approach has been tested by using a multiple expert systems structure based on using interactively two systems, an expert system development tool for design, KAUS, and an expert system development tool for diagnosing engineering processes, SUPER. The intrinsic properties of SUPER have also been used for improving the design procedure when qualitative and quantitative knowledge is involved.>

The contents of most databases are ever changing; and erroneous data can be a significant problem in real-world databases. Hence, the process of discovering knowledge from databases is a process based on incipient hypothesis generation/evaluation and refinement/management. Although many systems for knowledge discovery in databases have been proposed, most systems have not addressed the capabilities of refining/managing the discovered knowledge. This paper describes a system called IIBR (inheritance inference based refinement) for managing/refining the knowledge discovered from databases. IIBR is one of subsystems of the GLS (global learning scheme) discovery system, and can be cooperatively used with other subsystems of GLS such as KOSI (knowledge-oriented statistical inference). By means of IIBR, the structural characteristics denoted by regression models, which are discovered from a database by KOSI, can be added to a knowledge base as deductive rules and data sets for showing the rule errors, and can be managed and refined easily. IIBR is based on inheritance inference and error analysis, as well as the model-based representation of knowledge in the knowledge-based system KAUS.>

Introduces an approach called HML (hierarchical model learning) for refining and managing knowledge discovered from databases. HML is implemented as one of subsystems of the GLS (Global Learning Scheme) discovery system developed by us for knowledge discovery in databases. It is used as a learning phase in multiple learning phases of GLS. By means of HML, concept clusters which are discovered from a database can be added to a knowledge base as classification knowledge with hierarchical models and can be easily refined/managed. HML is based on the model representation of multi-layer logic (MLL). A key feature of HML is the quantitative evaluation for selecting the best representation of the MLL formula by cooperatively using a criterion based on information theory and domain knowledge. Experience with a prototype of HML implemented by the knowledge-based system KAUS is discussed.