Mediating between AI and highly specialized users

We report part of the design experience gained in X-Media, a system for knowledge management and sharing. Consolidated techniques of interaction design (scenario-based design) had to be revisited to capture the richness and complexity of intelligent interactive systems. We show that the design of intelligent systems requires methodologies (faceted scenarios) that support the investigation of intelligent features and usability factors simultaneously. Interaction designers become mediators between intelligent technology and users, and have to facilitate reciprocal understanding

The Relationship between Fuzzy Reasoning and Its Temporal Characteristics for Knowledge Management

The knowledge management systems based on artificial reasoning (KMAR) tries to provide computers the capabilities of performing various intelligent tasks for which their human users resort to their knowledge and collective intelligence. There is a need for incorporating aspects of time and imprecision into knowledge management systems, considering appropriate semantic foundations. The aim of this paper is to present the FRTES, a real-time fuzzy expert system, embedded in a knowledge management system. Our expert system is a special possibilistic expert system, developed in order to focus on fuzzy knowledge.Knowledge Management, Artificial Reasoning, predictability

Harnessing agile concepts for the development of intelligent systems

Traditional and current approaches to intelligent systems design, have led to the creation of sophisticated and computationally-intensive packages and environments, for a wide range of applications. This paper proposes methods with which to extend the functionality of such systems, borrowing knowledge management concepts from the field of Agile Manufacturing. As such, this paper proposes that the future of intelligent systems design should be based not only upon the continuing development of artificial intelligence techniques, but also effective methods for harnessing human skills and core competencies to achieve these aims

University Knowledge Management Tool for Academic Research Activity Evaluation

The implementation of an efficient university knowledge management system involves the de-velopment of several software tools that assist the decision making process for the three main activities of a university: teaching, research, and management. Artificial intelligence provides a variety of techniques that can be used by such tools: machine learning, data mining, text mining, knowledge based systems, expert systems, case-based reasoning, decision support systems, intelligent agents etc. In this paper it is proposed a generic structure of a university knowledge management system, and it is presented an expert system, ACDI_UPG, developed for academic research activity evaluation, that can be used as a decision support tool by the university knowledge management system for planning future research activities according to the main objectives of the university and of the national / international academic research funding organizations.University Knowledge Management, Research Activity Evaluation, Artificial Intelligence, Expert Systems, Decision Support System

An Intelligent Knowledge Management System from a Semantic Perspective

Abstract. Knowledge Management Systems (KMS) are important tools by which organizations can better use information and, more importantly, manage knowledge. Unlike other strategies, knowledge management (KM) is difficult to define because it encompasses a range of concepts, management tasks, technologies, and organizational practices, all of which come under the umbrella of the information management. Semantic approaches allow easier and more efficient training, maintenance, and support knowledge. Current ICT markets are dominated by relational databases and document-centric information technologies, procedural algorithmic programming paradigms, and stack architecture. A key driver of global economic expansion in the coming decade is the build-out of broadband telecommunications and the deployment of intelligent services bundling. This paper introduces the main characteristics of an Intelligent Knowledge Management System as a multiagent system used in a Learning Control Problem (IKMSLCP), from a semantic perspective. We describe an intelligent KM framework, allowing the observer (a human agent) to learn from experience. This framework makes the system dynamic (flexible and adaptable) so it evolves, guaranteeing high levels of stability when performing his domain problem P. To capture by the agent who learn the control knowledge for solving a task-allocation problem, the control expert system uses at any time, an internal fuzzy knowledge model of the (business) process based on the last knowledge model.knowledge management, fuzzy control, semantic technologies, computational intelligence

An Intelligent Knowledge Management System from a Semantic Perspective

Knowledge Management Systems (KMS) are important tools by which organizations can better use information and, more importantly, manage knowledge. Unlike other strategies, knowledge management (KM) is difficult to define because it encompasses a range of concepts, management tasks, technologies, and organizational practices, all of which come under the umbrella of the information management. Semantic approaches allow easier and more efficient training, maintenance, and support knowledge. Current ICT markets are dominated by relational databases and document-centric information technologies, procedural algorithmic programming paradigms, and stack architecture. A key driver of global economic expansion in the coming decade is the build-out of broadband telecommunications and the deployment of intelligent services bundling. This paper introduces the main characteristics of an Intelligent Knowledge Management System as a multiagent system used in a Learning Control Problem (IKMSLCP), from a semantic perspective. We describe an intelligent KM framework, allowing the observer (a human agent) to learn from experience. This framework makes the system dynamic (flexible and adaptable) so it evolves, guaranteeing high levels of stability when performing his domain problem P. To capture by the agent who learn the control knowledge for solving a task-allocation problem, the control expert system uses at any time, an internal fuzzy knowledge model of the (business) process based on the last knowledge model.knowledge management, fuzzy control, semantic technologies, computational intelligence

Information, knowledge and the context of interaction

Representing knowledge as information content alone is insufficient in providing us with an understanding of the world around us. A combination of context as well as reasoning of the information content is fundamental to representing knowledge, within information –based systems. The field of knowledge representation and knowledge management has thus far been concerned with providing structures and theories that can lead to some form of qualified intelligent reasoning, and contextualised information. By drawing upon previous research and applying and extending concepts of Semiotics and Symbiosis from the interaction design school of thought, this paper presents a conceptual framework for establishing the interplay between knowledge and users of knowledge via information systems constructs. Subsequently, by drawing upon notions of interfaces to knowledge, a conceptual framework which describes the relationship between the semiotic, symbiotic and interface to knowledge presented, along with a discussion of contemporary issues common to the field of knowledge management is developed

Flight elements: Fault detection and fault management

Fault management for an intelligent computational system must be developed using a top down integrated engineering approach. An approach proposed includes integrating the overall environment involving sensors and their associated data; design knowledge capture; operations; fault detection, identification, and reconfiguration; testability; causal models including digraph matrix analysis; and overall performance impacts on the hardware and software architecture. Implementation of the concept to achieve a real time intelligent fault detection and management system will be accomplished via the implementation of several objectives, which are: Development of fault tolerant/FDIR requirement and specification from a systems level which will carry through from conceptual design through implementation and mission operations; Implementation of monitoring, diagnosis, and reconfiguration at all system levels providing fault isolation and system integration; Optimize system operations to manage degraded system performance through system integration; and Lower development and operations costs through the implementation of an intelligent real time fault detection and fault management system and an information management system

A Prototype Intelligent Tutoring System for Programming Languages: Adding a User Model and Feedback to Instructional Transaction Shells

Intelligent tutoring systems and knowledge management systems are becoming increasingly important, especially their connection to training-on-demand systems. New developments like the Internet and advances in expert systems allow the deployment of intelligent interfaces, which can present the knowledge accumulated in a database. This paper proposes a prototype that utilizes these new technologies to extend Merrill’s work (1992) on instructional transaction shells. These shells embody instructional techniques that are independent of the knowledge they are teaching, and thus reusable. The prototype includes a self-learning expert system for user modeling, thus allowing for a personalized user interface and presentation of content

RECONCILING KNOWLEGDE MANAGEMENT AND E-COLLABORATION SYSTEMS: THE INFORMATION-DRIVEN KNOWLEDGE MANAGEMENT FRAMEWORK

In recent years, e-collaboration systems have emerged as an essential enabler of communication and collaboration between enterprises. Current trends in the area of e-collaboration emphasize the importance of effective collaborative knowledge management support in e-collaboration systems. Our research aims at proposing an intelligent infrastructure for the reconciliation of knowledge management and e-collaboration systems. The objective of the paper is to introduce a conceptual framework for designing and building the new infrastructure that supports specific characteristics of collaborative knowledge management in e-collaboration systems. The paper articulates how this framework enables efficient knowledge exploration and exploitation, before concluding with implications and recommendations for future developments in this area