How to use knowledge in an analysis process

The need to create mechanisms or systems to acquire, represent and convey knowledge is one of the problems that has been given priority in the passing of the years of research, the need for acquisition has generated the need for more compact models that conserve implicit and explicit knowledge generated within their organizations. In this environment, due to the change that has generated knowledge management, companies recognize the importance of the management of these intangible assets, as a requirement to stay in the market and adapt to organizational and technological changes, each organization must find its own way and recognize that intellectual capacity has become an important factor for the success of organizational development. The aim of this paper is to present the results of a systematic review of the literature by which we identify some acquisition techniques and knowledge representation, that can be replicated in organizations to leverage internal and external knowledge to the organization and achieve advantages sustainable competitive . The results present management techniques related to the culture and values organizational as well as the definition of different types of alliances also identified as expert systems technologies and social tools as useful for the acquisition and representation of knowledge.

Rough set based lattice structure for knowledge representation in medical expert systems: Low back pain management case study

A New Approach In Learning For Intelligent Multi Agent Systems

In the era of big data, knowledge engineering faces fundamental challenges induced by fragmented knowledge from heterogeneous, autonomous sources with complex and evolving relationships. The knowledge representation, acquisition, and inference techniques developed in the 1970s and 1980s, driven by research and development of expert systems, must be updated to cope with both fragmented knowledge from multiple sources in the big data revolution and in-depth knowledge from domain experts. This article presents BigKE, a knowledge engineering framework that handles fragmented knowledge modeling and online learning from multiple information sources, nonlinear fusion on fragmented knowledge, and automated demand-driven knowledge navigation.

Dutch research on knowledge-based instructional systems: Introduction to the special issue

The knowledge representation and reasoning strategies in an automatic program synthesis architecture called ELF are described. ELF synthesizes computer-aided design (CAD) tools that automatically route wires in VLSI circuits. The design space ELF confronts, requires it to understand various physical technologies, to select an appropriate procedure-level decomposition, to choose algorithms and data structures, to manage any interdependencies, and to generate efficient code. ELF manages the design space using a variety of knowledge sources, including domain-specific knowledge. The manner in which knowledge is used determines the representation method of choice. The effectiveness of these ideas is illustrated via a tour through the synthesis steps for a specific routing tool, and a brief discussion of the performance of the resulting synthetic router as measured against an industrial tool.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In order to solve the problems of complicated design process, insufficient optimization of design parameters and low reuse of design knowledge during traditional mechanical product detailed design process, this paper proposes a rule-driven product configuration design method. Traditional mechanical product design process is divided into a number of design nodes and partially complex functions of traditional design process are defined in nodes. Design nodes are configured and connected after forming rule-driven nodes ,which sharply simplifies the configuration process of traditional design process and can realize the optimization of design parameters. The basic database that adapt to the design node is developed and the design knowledge is integrated into the basic database and node rules, which improves the configurability of the design process and better supports the parameter optimization of the design process and reuse of design knowledge. Finally, the system software is developed and the design process of Marine diesel turbocharger is taken as an example to verify the effectiveness of the method.

Planning and scheduling problems have become a promising area of research in the field of artificial intelligence (AI). In order to provide collision avoidance and enable successful completion of the task, the robot requires sophisticated knowledge about the environment for processing the tasks or methods. Knowledge representation and reasoning (KR & R) techniques provide such a level of knowledge to perceive the task environment. These techniques include description logic (DL) and ontology-based approaches. DL provides low-level knowledge to describe an environment. With advances in KR using ontology, high-level knowledge domain descriptions can be obtained more effectively. Knowledge representation techniques aid in temporal, ontological and spatial reasoning in a modeled environment. In this chapter, low-level KR & R techniques for task planning are summarized.

Processes of knowledge and software

A RFBSE model for capturing engineers’ useful knowledge and experience during the design process

In this paper, we present a model-based expert system for automatic digital systems design. The goal of digital systems design is to generate a workable and efficient design from high level specifications. The formalization of the design process is a necessity for building an efficient automatic CAD system. Our approach combines model-based, heuristic best-first search, and meta-planning techniques from AI to facilitate the design process. The design process is decomposed into three subprocesses. First, the high-level behavioral specifications are translated into sequences of primitive behavioral operations. Next, primitive operations are grouped to form intermediate-level behavioral functions. Finally, structural function modules are selected to implement these functions. Using model-based reasoning on the primitive behavioral operations level extends the solution space considered in design and provides more opportunity for minimization. Heuristic best-first search and meta-planning tech-niques control the decision-making in the latter two subprocesses to optimize the final design. They also facilitate system maintenance by separating design strategy from design knowledge.

: This report is the by-product of information collected by the authors during research into expert system technology conducted at the Air Force Institute of Technology. That research involved methods for selecting appropriate tools (or knowledge acquisition techniques) to collect information from experts. In the course of the research, the authors discovered that no single publication discussed all of the collection techniques that a knowledge engineer might want to evaluate. This brief report attempts to remedy that deficiency by consolidating into one document the primary knowledge acquisition techniques used today. For each technique, the authors have provided a short description, evaluation, and bibliography for individuals who want to evaluate a technique in greater depth. The discussion of techniques is introduced by an overview of some issues and architectures of expert system design.

Different knowledge representation tasks require different knowledge representation techniques. Agent designers should therefore be able to easily exploit benefits of various knowledge representation technologies in a single agent system.I describe here a modular agent programming language Jazzyk based on the programming framework of Behavioural State Machines (BSM). BSM framework, and thus also Jazzyk, draws a strict distinction between a knowledge representational and a behavioural level of an agent program. It supports a high degree of modularity w.r.t. employed KR technologies, and at the same time provides a clear and concise semantics.

Design for energy efficiency in early stages: A top-down method for new product development

Museums and science centres are informal education environments that intend to engage visitors with their exhibits.We present an efficient design process that allows an improved working relationship between museum practitioners, exhibition designers, and visitors. Its principles and a graphical representation are based on the Engagement Profile from previous work. Curators and designers evaluate the qualities of exhibits using the engagement profile while visitors inform the design process by answering a standardised questionnaire. Elements of the design process were evaluated using an educational game at the science centre Engineerium. This study was based on the responses from over five hundred visitors to the science centre. As an outcome, the science centre received valuable information of how to improve the exhibit. Further, one iteration of the design process was implemented in practice. First experiences show that our method will practically support the work of museum practitioners and exhibition designers. We present the principles and a graphical representation based on the Engagement Profile from previous work. Elements of the design process are evaluated using an educational game at the science centre Engineerium. The evaluation is based on a study with over five hundred visitors to the science centre.