Towards a temporal coherence management in real-time knowledge-based systems

Response time satisfaction in a real-time knowledge-based system

Real-time knowledge-based systems (KBSs) are increasingly used to monitor and control real-time processes. Although a KBS may perform well on the average in terms of computation time, it may fail to deliver results while meeting deadlines in some critical cases. This is not acceptable in a hard real-time system where all timing constraints imposed by the environment must be continuously observed by the embedded computer and the failure to meet a single deadline may be catastrophic. Thus the technology for building a new generation of real-time KBSs must be developed before we can rely on these smart systems to perform complex, safety-critical monitoring and control functions in a real-time environment as we currently do on embedded software programs implemented in procedural languages. In this article, we examine some fundamental issues in the design and development of real-time KBSs including (1) response time analysis, and (2) rule-base optimization and synthesis. >

The application of a supervisory knowledge-based system (KBS) to provide robust multivariable control of a chemical reaction process with reduced conservatism is examined. The supervisory KBS is capable of monitoring the process to detect process and control system faults or deteriorating control system performance due to changes in the process behavior or operating conditions. If a fault or change in performance is detected, the KBS formulates and implements the necessary corrective action. Control loop tuning is emphasized. The underlying mechanisms are discussed and validation results, using a rigorous simulation of the chemical reactor process, are presented.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Knowledge-based (KB) technology failed to provide a number of features significant for applications in real-time environments. Real-time KB systems have to provide support for making tradeoffs between processing power, response time, data space, inattention and degradation. There is also a problem of time semantics which have to be included in the knowledge representation.

Collaborative design conflicts are an important type of process “exception”, i.e. a reallife contingency such as a process change, execution error or missed opportunity that leads to sub-optimal performance of a collaborative process. This chapter presents an integrated computational approach to collaborative process exception handling that avoids important weaknesses in current conflict management methods through the synergistic integration of conflict, workflow and rationale technology. The approach is based on an inclusive dependency language plus co ordination services for dependency capture, process enactment and exception handling. An initial implementation of this model called “iDCSS” is presented and challenges for future evolution of this technology are identified.

A novel approach is described for building intelligent information systems (or knowledge-base management systems). The approach utilizes the knowledge data language, which is a schema specification language developed for the knowledge/data model. The model, referred to as a hypersemantic data model, captures both knowledge semantics, as specified in knowledge-based systems, and data semantics, as represented by semantic data models. Hypersemantic data models facilitate the incorporation of knowledge in the form of heuristics, uncertainty, constraints and other artificial intelligence concepts, together with object-oriented concepts found in semantic data models. The unified knowledge/data modeling features and constructs of the language are used to develop a prototype knowledge base management system, the KDL-advisor. >

Ever since MYCIN introduced the idea of computer-based explanations to the artificial intelligence community, it has come to be taken for granted that all knowledge-based systems (KBS) need to provide explanations. The first part of this dissertation proposes a cognitive learning theory based model that both clarifies the reasons as to why KBS need to provide explanations and serves as the basis for conceptualizing the provision of KBS explanations. Using the concepts of the feedforward and feedback operators of cognitive learning it develops strategies for providing KBS explanations and uses them to classify the various types of explanations found in current KBS applications. The roles of feedforward and feedback explanations within the context of the theory of cognitive skill acquisition and a model of expert judgment are also analyzed. These, together with past studies of KBS explanations, suggest that user expertise, the types of explanations provided, and the level of user agreement are significant factors that influence the explanation seeking behavior of users. The dissertation also explores the effects of the use of KBS explanations in judgmental decision making situations supported by a KBS. It identifies and considers four distinct categories of potential effects of the use of explanations--learning effects, perceived effects, behavioral effects, and effects on judgmental decision making. The second part of the dissertation empirically evaluates the explanation provision strategies in a laboratory experiment in which 80 novice and expert subjects used a KBS for financial analysis to make judgments under conditions of uncertainty. The experiment was designed specifically to investigate the following fundamental research questions: (1) To what extent are the various kinds of explanations used? (2) How does user expertise, the feedforward and feedback provision of explanations, and the level of user agreement influence the amount and the types of explanations that are used? and (3) Does the use of explanations affect the accuracy of judgmental decision-making and user perceptions of usefulness? Some of the major results relating to the determinants of the use of KBS explanations include: (1) user expertise is not a determinant of the proportion of explanations used but influences the types of explanations that are used, (2) explanation provision strategy is a critical determinant of the use of KBS explanations with feedback explanations being used significantly more than feedforward explanations, and (3) the three types of explanations are used in different proportions with the Why and How explanations being used significantly more that the Strategic explanations. (Abstract shortened by UMI.)

This work is concerned with the design and construction of a yarn brushing machine to produce and monitor such yarns by means of the application of knowledge-based, computer vision and on-line control systems. Part I describes an investigation into the effects that input yarn properties and changes in machine parameters have on the characteristics of the brushed yarn.

It is evident that the brain is capable of large-scale reorganization following sensory deprivation, but the extent of such reorganization is to date, not clear. The auditory modality is the most accurate to represent temporal information, and deafness is an ideal clinical condition to study the reorganization of temporal representation when the audio signal is not available. Here we show that hearing, but not deaf individuals, show a strong ERP response to visual stimuli in temporal areas during a time-bisection task. This ERP response appears 50–90 ms after the flash and recalls some aspects of the N1 ERP component usually elicited by auditory stimuli. The same ERP is not evident for a visual space-bisection task, suggesting that the early recruitment of temporal cortex is specific for building a highly resolved temporal representation within the visual modality. These findings provide evidence that the lack of auditory input can interfere with typical development of complex visual temporal representations.

In Memoriam: Dr. Clive L. Dym (1942–2016)

At the time being knowledge-based systems are used in almost all life aspects. The main reason for trying to use knowledge-based systems in real-time control is to reduce cognitive load on users (overload), their application proving to be important when conventional techniques have failed or are not sufficiently effective [1]. The development of automated diagnosis techniques and systems can help also to minimize downtime and maintain efficient output. This paper presents some issues of applying knowledge-based systems to real-time control systems. It describes and analyzes the main issues concerning the real-time domain and provides possible solutions, such as a set of requirements that a real-time knowledge-based system must satisfy. The paper proposes a possible architecture for applying knowledge-based techniques in real-time control systems. Finally, a way of employing knowledge-based techniques for extending the existing automatic control and monitoring system for the geothermal plant from the University of Oradea is presented.

This paper presents ontology for temporal and fuzzy information representation in the context of relational database environment. Temporal information has its own semantics and there are approaches to deal with representation of temporal data in different database environments. Temporal data may not be precise or crisp in nature, there is an element of imprecision or vagueness in representing temporal attributes. This paper proposes general model for temporal and fuzzy ontology for building fuzzy-temporal database schema with illustrations.

The tax big data supervision system and method, including the distributed real-time management and control system deployed in the superior unit and each subordinate unit, the superior distributed real-time management and control system is used to receive the management and control rules, and issue the management and control rules to the subordinate distributed real-time management and control System: The distributed real-time management and control system writes the massive data in the data source at the same level into the distributed message queue Kafka in real time, and uses the distributed stream computing engine Flink to compare and calculate based on the management and control rules, and write the data that conforms to the management and control rules. Enter the alarm information into Kafka; the lower-level distributed real-time management and control system pushes the alarm information in the Kafka subscription at this level to Kafka in the upper-level distributed real-time management and control system; the upper-level distributed real-time management and control system pushes the alarm information in the Kafka subscription to users , at the same time push the alarm information to the distributed search engine ElasticSearch to save, and provide the historical alarm information query function.

A knowledge-based system (or an expert system) embodies a domain expert’s knowledge and problemsolving strategy. Domain specific knowledge-based systems formalise the complicated set of informal procedures understood by experts alone and make the expertise available to a wide range of people. Knowledgebased systems have been developed and used in fields as diverse as medicine, mineral exploration, and chemistry. They have been used in various engineering applications and extensively in manufacturing. This paper reviews the application of knowledge-based systems in the Printed Circuit Board (PCB) assembly domain. In keeping with the initial development of knowledgebased systems in the field of manufacturing, most of the current applications of knowledge-based systems in PCB assembly address issues related to process planning. This trend can be attributed to the diversity and short life cycles of electronic products, and shortage of human expertise. A few systems have been developed for PCB design. Developments in the field of electronics packaging and industry predictions lead us to believe that knowledge-based systems will be developed for many more applications within the field.

The processing cost of queries with an empty answer, both in the database and knowledge base context, is usually high. One purpose of semantic query optimization methods in the database context is to use semantic knowledge to detect such types of queries. Although semantic query optimization is well known in the database context, this is not the case for knowledge base systems (KBSs). This paper presents a method that allows the detection of queries with an empty answer using only semantic information expressed in the knowledge base definition. The method can be applied in the context of KBSs that provide some of the following features: structuring mechanisms, assertional knowledge, temporal information, and handling of inequality expressions. >