Application of hybrid expert systems in Computer Integrated Manufacturing

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

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.

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.

Answer set Prolog, or AnsProlog in short, is one of the leading knowledge representation (KR) languages with a large body of theoretical and building block results, several implementations and reasoning and declarative problem solving applications. But it shares the problem associated with knowledge acquisition with all other KR languages; most knowledge is entered manually by people and that is a bottleneck. Recent advances in natural language processing have led to some systems that convert natural language sentences to a logical form. Although these systems are in their infancy, they suggest a direction to overcome the above mentioned knowledge acquisition bottleneck. In this paper we discuss some recent work by us on developing applications that process logical forms of natural language text and use the processed result together with AnsProlog rules to do reasoning and problem solving.

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

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.

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.

Parallel associative networks, or ‘‘PANs’’, are an information processing mechanism based on formal neuron networks. The IBM Palo Alto Scientific Center is exploring the nature and possible computing applications of PANs. This work has led to the creation of a versatile experimentation environment called the ‘‘PAN workstation’’. The workstation includes a parallel PAN emulation processor (the Network Emulation Processor, or ‘‘NEP’’) which greatly speeds up the processing of large PAN nets. Our initial application studies have dealt with low complexity functions such as binary and graded logic gates, as well as applications in robotics vision and expert systems. Our current research is in the area of PAN‐based knowledge representation and inference mechanisms.

Processing natural language such as English has always been one of the central research issues of artificial intelligence, both because of the key role language plays in human intelligence and because of the wealth of potential applications. Many of the knowledge representation and inference techniques that have been applied successfully in knowledge-based systems were originally developed for processing natural language, but the language-processing applications themselves have always seemed far from being realized. The special series on natural-language processing is an attempt to bring language processing and its applications into focus/spl minus/to demonstrate techniques that have recently been applied to real-world problems, to identify research ripe for practical exploitation, and to illustrate some promising combinations of natural-language processing with other emerging technologies. Each of the four articles in the series provides some insight into the state of the art and conveys the practical significance of recent research in the field. >

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.

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.

Turbo Expert, a knowledge representation and processing language, is the basic support language for expert system construction. Turbo Expert has been implemented for IBM PC and for El'brus multiprocessor systems.

In many complex domains, the input data are often not suited for the typical vector representations used in deep learning models. For example, in knowledge representation, relational learning, and some computer vision tasks, the data are often better represented as graphs or sets. In these cases, a key challenge is to learn a representation function which is invariant to permutations of set or isomorphism of graphs. In order to handle graph isomorphism, this thesis proposes a subgraph pattern neural network with invariance to graph isomorphisms and varying local neighborhood sizes. Our key insight is to incorporate the unavoidable dependencies in the training observations of induced subgraphs into both the input features and the model architecture itself via high-order dependencies, which are still able to take node/edge labels into account and facilitate inductive reasoning. In order to learn permutation-invariant set functions, this thesis shows how the characteristics of an architecture’s computational graph impact its ability to learn in contexts with complex set dependencies, and demonstrate limitations of current methods with respect to one or more of these complexity dimensions. I also propose a new Self-Attention GRU architecture, with a computation graph that is built automatically via self-attention to minimize average interaction path lengths between set elements in the architecture’s computation graph, in order to effectively capture complex dependencies between set elements.Besides the typical set problem, a new problem of representing sets-of-sets (SoS) is proposed. In this problem, multi-level dependence and multi-level permutation invariance need to be handled jointly. To address this, I propose a hierarchical sequence attention framework (HATS) for inductive set-of-sets embeddings, and develop the stochastic optimization and inference methods required for efficient learning.

Knowledge representation with SOUL