Using Electronic Documents for Knowledge Acquisition and Model Maintenance

Knowledge acquisition and model maintenance are key problems in knowledge engineering to improve the productivity in the development of intelligent systems. Although historically a number of technical solutions have been proposed in this area, the recent experience shows that there is still an important gap between the way end-users describe their expertise and the way intelligent systems represent knowledge. In this paper we propose an original way to cope with this problem based on electronic documents. We propose the concept of intelligent document processor as a tool that allows the end-user to read/write a document explaining how an intelligent system operates in such a way that, if the user changes the content of the document, the intelligent system will react to these changes. The paper presents the structure of such a document based on knowledge categories derived from the modern knowledge modeling methodologies together with a number of requirements to be understandable by end-users and problem solvers

Modeling human behavior in user-adaptive systems: recent advances using soft computing techniques

Adaptive Hypermedia systems are becoming more important in our everyday activities and users are expecting more intelligent services from them. The key element of a generic adaptive hypermedia system is the user model. Traditional machine learning techniques used to create user models are usually too rigid to capture the inherent uncertainty of human behavior. In this context, soft computing techniques can be used to handle and process human uncertainty and to simulate human decision-making. This paper examines how soft computing techniques, including fuzzy logic, neural networks, genetic algorithms, fuzzy clustering and neuro-fuzzy systems, have been used, alone or in combination with other machine learning techniques, for user modeling from 1999 to 2004. For each technique, its main applications, limitations and future directions for user modeling are presented. The paper also presents guidelines that show which soft computing techniques should be used according to the task implemented by the application

Mining HCI Data for Theory of Mind Induction

Human-computer interaction (HCI) results in enormous amounts of data-bearing potentials for understanding a human user’s intentions, goals, and desires. Knowing what users want and need is a key to intelligent system assistance. The theory of mind concept known from studies in animal behavior is adopted and adapted for expressive user modeling. Theories of mind are hypothetical user models representing, to some extent, a human user’s thoughts. A theory of mind may even reveal tacit knowledge. In this way, user modeling becomes knowledge discovery going beyond the human’s knowledge and covering domain-specific insights. Theories of mind are induced by mining HCI data. Data mining turns out to be inductive modeling. Intelligent assistant systems inductively modeling a human user’s intentions, goals, and the like, as well as domain knowledge are, by nature, learning systems. To cope with the risk of getting it wrong, learning systems are equipped with the skill of reflection

Toward domain-specific design environments: Some representation ideas from the telecommunications domain

ACME is an experimental environment for investigating new approaches to modeling and analysis of system requirements and designs. ACME is built on and extends object-oriented conceptual modeling techniques and knowledge representation and reasoning (KRR) tools. The most immediate intended use for ACME is to help represent, understand, and communicate system designs during the early stages of system planning and requirements engineering. While our research is ostensibly aimed at software systems in general, we are particularly motivated to make an impact in the telecommunications domain, especially in the area referred to as Intelligent Networks (IN's). IN systems contain the software to provide services to users of a telecommunications network (e.g., call processing services, information services, etc.) as well as the software that provides the internal infrastructure for providing the services (e.g., resource management, billing, etc.). The software includes not only systems developed by the network proprietors but also by a growing group of independent service software providers

A rapid prototyping/artificial intelligence approach to space station-era information management and access

Applications of rapid prototyping and Artificial Intelligence techniques to problems associated with Space Station-era information management systems are described. In particular, the work is centered on issues related to: (1) intelligent man-machine interfaces applied to scientific data user support, and (2) the requirement that intelligent information management systems (IIMS) be able to efficiently process metadata updates concerning types of data handled. The advanced IIMS represents functional capabilities driven almost entirely by the needs of potential users. Space Station-era scientific data projected to be generated is likely to be significantly greater than data currently processed and analyzed. Information about scientific data must be presented clearly, concisely, and with support features to allow users at all levels of expertise efficient and cost-effective data access. Additionally, mechanisms for allowing more efficient IIMS metadata update processes must be addressed. The work reported covers the following IIMS design aspects: IIMS data and metadata modeling, including the automatic updating of IIMS-contained metadata, IIMS user-system interface considerations, including significant problems associated with remote access, user profiles, and on-line tutorial capabilities, and development of an IIMS query and browse facility, including the capability to deal with spatial information. A working prototype has been developed and is being enhanced

Intelligent Decision Support Systems- A Framework

Information technology applications that support decision-making processes and problem- solving activities have thrived and evolved over the past few decades. This evolution led to many different types of Decision Support System (DSS) including Intelligent Decision Support System (IDSS). IDSS include domain knowledge, modeling, and analysis systems to provide users the capability of intelligent assistance which significantly improves the quality of decision making. IDSS includes knowledge management component which stores and manages a new class of emerging AI tools such as machine learning and case-based reasoning and learning. These tools can extract knowledge from previous data and decisions which give DSS capability to support repetitive, complex real-time decision making.  This paper attempts to assess the role of IDSS in decision making. First, it explores the definitions and understanding of DSS and IDSS. Second, this paper illustrates a framework of IDSS along with various tools and technologies that support it. Keywords: Decision Support Systems, Data Warehouse, ETL, Data Mining, OLAP, Groupware, KDD, IDS