Systematic review of knowledge acquisition and representation techniques

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.

An efficient design and analysis process is based on an enormous amount of knowledge and information. Therefore, tools and techniques for knowledge acquisition, representation, and visualisation have to be integrated into the overall design process but they don't have to be treated as separate components. The variety of knowledge sources and its dependency on the environment lead to a complex integration task. This paper presents a knowledge enriched approach to support a signal integrity analysis process.

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

An Artificial Intelligence program to advise physicians regarding antimicrobial therapy

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

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.

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.

Knowledge representation with SOUL

Based on a detained discussion of system architectures for knowledge based speech understanding and knowledge representation techniques, criteria for both a knowledge representation scheme and system architectures are developed. Upon this background a system is introduced which is organized around a homogenuous knowledge base. Both the knowledge representation language and the content of the knowledge base are described. The knowledge representation language do not only cover declarative but also procedural knowledge. Analysis processes are guided by a flexibel bottom-up top-down strategy. Besides the procedural semantics of the language the A -Algorithm is used for this purpose. Search graph nodes are judged by a vector which reflects knowledge dependent and acoustic scores and which is admissable for the A -Algorithm.

At present, software development life cycle has many standards involved. However, during the process of creating components in an application, there are many problems that originate in the knowledge gap between stakeholders and developers. The problem of developing is issues such as ambiguity, misunderstanding, miscommunications, different domain skills, and conflicting goals. Metamodel are needed because the methodology contents must be purpose-fit and optimally adjusted for the users/stakeholders/organisation. Metamodel, a model of software model includes endeavor domain, method domain, and metamodel domain. It has contents for user's needs, including organization, project, and product aspect. Three interconnected meta-elements are work products, work units, and producers, as well as relationship between them. ISO/IEC 24744 is the standard that includes metamodels.Application software are now applied knowledge management components aiming to reduce the gap between users/stakeholders and software developer team. The knowledge can be shared and transferred to others. This paper, therefore, focuses on applying knowledge management metamodel for software development process. We will extend the metamodel concept in ISO/IEC 24744 for knowledge acquisition, knowledge representation, and knowledge transfer to WorkUnits and WorkProducts, thereby increasing their explicitness in the software development process. Tacit knowledge will be focused and propose knowledge metamodel will be shown.

Artificial intelligence can assist in industrial control in many ways, but this paper concentrates on the role of knowledge-based systems in the areas of control, detection and diagnosis of faults, scheduling and planning. The paper argues that the application of knowledge-based techniques can provide a unification of these and other activities, and outlines the requirements of knowledge representation and inference techniques that are needed to make such systems a reality. The advances in hardware, including sensors as well as computers, that will be driven by and in turn stimulate the introduction of the knowledge-based industrial control systems are also considered.

This paper reviews the development and implementation of a number of approximate reasoning models for production planning and control, inventory capacity and spare parts planning and control, hierarchical-multi-objective scheduling and control etc., to improve the effectiveness of managerial decision support systems in integrated AI—OR models. These approximate reasoning models are based on knowledge representation and inference techniques of fuzzy sets and fuzzy logic to capture environmental uncertainties and managerial expertise in terms of flexible natural language expressions. Such computer system models are ‘user friendly’. They do not require precise information. Ordinal scale strength, which is a minimal data characteristic, is sufficient for such model development and implementation. They may be based on expert knowledge obtained either through real-life experiences or a sophisticated OR model analysis.Key wordsfuzzy setsplanningschedulingmanagement

This paper presents an enhancement of the CARESS system—A Constraint Approximative Reasoning System Support—introduced in (Popescu and Roventa, 1994). CARESS is an experimental system with primarily two objectives: (1)knowledge representation and manipulation techniques and to implement them in PROLOG III, and (2) to develop a knowledge programming environment for building expert systems. We discuss here the use of meta-programming, constraint logic programming and approximate reasoning for the design of expert systems It has already been proven that meta-programming and logic programming are powerful techniques for expert system design. Fuzzy logic can be used to model one kind of uncertainty. Constraint logic programming is useful for dealing with the constraints given by operations using fuzzy sets.

In this Chapter we focus on a problem of a natural language access to a database, well-known and highly desired to be solved. We start with the modern approaches based on deep learning and analyze lessons learned from unusable database access systems. This chapter can serve as a brief introduction to neural networks for learning logic representations. Then a number of hybrid approaches are presented and their strong points are analyzed. Finally, we describe our approach that relies on parsing, thesaurus and disambiguation via chatbot communication mode. The conclusion is that a reliable and flexible database access via NL needs to employ a broad spectrum of linguistic, knowledge representation and learning techniques. We conclude this chapter by surveying the general technology trends related to NL2SQL, observing how AI and ML are seeping into virtually everything and represent a major battleground for technology providers.