Real-time Knowledge-based Systems Research Articles

The Impact of Artificial Intelligence on Branding

Understanding the growth paths of artificial intelligence (AI) and its impact on branding is extremely pertinent of technology-driven marketing. This explorative research covers a complete bibliometric analysis of the impact of AI on branding. The sample for this research included all 117 articles from the period of 1982-2019 in the Scopus database. A bibliometric study was conducted using co-occurrence, citation analysis and co-citation analysis. The empirical analysis investigates the value propositions of AI on branding. The study revealed the nine clusters of co-occurrence: Social Media Analytics and Brand Equity; Neural Networks and Brand Choice; Chat Bots-Brand Intimacy; Twitter, Facebook, Instagram-Luxury Brands; Interactive Agent-Brand Love and User Choice; Algorithm Recommendations and E-Brand Experience; User-Generated Content-Brand Sustainability; Brand Intelligence Analytics; and Digital Innovations and Brand Excellence. The findings also identify four clusters of citation analysis—Social Media Analysis and Brand Photos, Network Analysis and E-Commerce, Hybrid Simulating Modelling, and Real-time Knowledge-Based Systems—and four clusters of co-citation analysis: B2B Technology Brands, AI Fostered E-Brands, Information Cascades and Online Brand Ratings, and Voice Assistants-Brand Eureka Moments. Overall, the study presents the patterns of convergence and divergence of themes, narrowing to the specific topic, and multidisciplinary engagement in research, thus offering the recent insights in the field of AI on branding.

Intelligent process monitoring by interfacing knowledge-based systems and multivariate statistical monitoring

An intelligent process monitoring and fault diagnosis environment has been developed by interfacing multivariate statistical process monitoring (MSPM) techniques and knowledge-based systems (KBS) for monitoring multivariable process operation. The real-time KBS developed in G2 is used with multivariate SPM methods based on canonical variate state space (CVSS) process models. Fault detection is based on T 2 charts of state variables. Contribution plots in G2 are used for determining the process variables that have contributed to the out-of-control signal indicated by large T 2 values, and G2 Diagnostic Assistant (GDA) is used to diagnose the source causes of abnormal process behavior. The MSPM modules developed in Matlab are linked with G2. This intelligent monitoring and diagnosis system can be used to monitor multivariable processes with autocorrelated, crosscorrelated, and collinear data. The structure of the integrated system is described and its performance is illustrated by simulation studies.

Intelligent Monitoring by Interfacing Multivariate Statistical Monitoring and Knowledge-Based Systems

An intelligent process monitoring and fault diagnosis environment has been developed by interfacing multivariate statistical process monitoring (MSPM) techniques and knowledge-based systems (KBS) for monitoring multivariable process operation. The real-time KBS developed in G2 is used with multivariate SPM methods based on canonical variate state space (CVSS) process models. Fault detection is based on T2 charts of state variables, contribution plots in G2 are used for determining the process variables that have contributed to the out-of-control signal indicated by large T2 values, and G2 Diagnostic Assistant (GDA) is used to diagnose the source causes of abnormal process behavior. The MSPM modules developed in Matlab are linked with G2. This setup extends the statistical process control library of GDA significantly and permits the use of MSPM tools for autocorrelated data and multivariable processes. The structure of the integrated system is described and its performance is illustrated by simulation studies.

Intelligent Sensor Auditing by Interfacing Knowledge-Based Systems and Multivariate Spm Tools

An intelligent sensor monitoring and auditing environment has been developed by interfacing multivariate statistical process monitoring (MSPM) techniques and knowledge-based systems (KBS) for monitoring sensor status in multivariable processes. The real-time KBS development environment G2 is integrated with an MSPM method that can monitor multivariable dynamic processes with autocorrelated data. The MSPM Inethod is based on a canonical variate state space (CVSS) process model of a dynamic process and it is used for auditing sensor status for bias, drift and excessive noise affecting the sensors of multivariable continuous processes. Changes in the magnitudes of means and variances of residuaLs between measured and predicted process variables are used to detect and discriminate sensor abnormalities. The statistical model that describes the in-control variability of sensor readings is based on a CVSS model. The CV state variables obtained from the state space model are linear combinations of the past process measurements which explain the variability of the future measurements the most, and they are regarded as the principal dynamic dimensions. The method can detect and discriminate between bias change, drift, and variations in noise levels of process sensors based on the analysis of data batches. The MSPM modules are developed in Matlab, cOnverted to C, and linked with G2. The presentation will focus on the structure and performance of the integrated system and illustration of the methodology by simulation studies.

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

The multi-agent system paradigm emerges as an interesting approach in the Knowledge-Based System (KBS) field when distributed problem-solving techniques are required. On the other hand, temporal representation and reasoning problems arise in a wide range of KBS application areas where time plays a crucial role. In this paper, we show that when agents run concurrently and access common temporal data, some problems of coherence arise. We analyse the different cases in which an incoherence in temporal information can occur and provide a method to tackle this problem. In this method, conflict management is handled by means of exception handlers and control rules allowing the users to explicitly define their own strategy for temporal coherence solving.

Intelligent Process Monitoring by Interfacing Knowledge-Based Systems and Multivariate SPC Tools

An intelligent process monitoring and fault diagnosis environment has been developed by interfacing multivariate statistical process monitoring (MSPM) techniques and knowledge-based systems (KBS) for monitoring multivariate process operation. In particular, the real-time KBS G2 and its Diagnostic Assistant (GDA) tool are used with multivariate SPM methods based on canonical variate state space (CVSS) process models. Fault detection is based on T 2 charts of state variables, contribution plots in G2 are used for determining the process variables that have contributed to the out-of-control signal indicated by large T 2 values, and GDA is used to diagnose the source cause of the abnormal process behavior. The MSPM modules developed in Matlab are linked with G2 and GDA. This setup extends the Statistical Process Control (GSPC) library of GDA significantly and permits the use of MSPM tools for autocorrelated data and multivariable processes. The presentation will focus on the structure and performance of the integrated system. On-line SPM of multivariable processes will be illustrated by simulation studies.

From the Synchronous Approach to Hybrid Systems

In this paper we present the use of RETIKS, a framework for the construction of Real-Time Knowledge-Based Systems, in the case of hybrid applications. RETIKS follows the production rules paradigm for knowledge representation, and the synchronous approach for time processing. RETIKS calculus is a special temporal calculus, and the compilation process transforms a given set of rules into a finite automata for execution. Temporal restrictions can be verified directly on the execution automata. Hybrid systems are characterized by a non-trivial mixing of discrete and continuous components. RETIKS can be applied in hybrid systems for event exchange and synchronization, and the continuous variables processing is implemented by a set of external functions. The application of the proposal is done using the steam boiler example.

Developing real-time knowledge-based systems with PERFECT

PERFECT (Programming EnviRonment For Expert systems Constrained in reasoning Time) is aimed at providing the necessary engineering support in real-time knowledge-based system development. PERFECT bridges the gap between the traditional analysis and design methodologies, and the implementation tools for these systems. It does so by providing the means to construct a knowledge model and to choose a suitable inference strategy. Subsequently the properties of the knowledge model and inference strategy may be analysed. For instance, it may be checked whether the knowledge model contains sufficient knowledge to diagnose a fault in an industrial process. Moreover, it may be checked whether the inference engine is able to provide an answer to a certain problem in time. If not, the analyser of PERFECT proposes an alternative structure of the knowledge model. When the constructed knowledge model and the chosen inference strategy show the required time efficiency, the compiler of PERFECT may translate them to an actual real-time knowledge based system in COGSYS. In addition, guidelines are provided with respect to the design of the human-machine interface. The resulting system is an instrument—a source of information that can be used by the human operator during problem-solving, rather than a prosthesis—a device that solves the entire problem by itself and presents the outcome to the human operator.

Distributed problem solving and real-time mechanisms in robot architectures

Autonomous robots operating in unstructured environments must react predictably to unexpected events, according to some form of specification. Mechanisms included in the design of the functional system architecture for an autonomous robot can help guarantee that such specifications are met. Examples of specifications include behaviours, meeting deadlines after events, or control performance. This paper considers the design of robot architectures using concepts from distributed problem solving (DPS) and real-time knowledge based systems (RTKBS). Qualitative performance measures are identified (coherency, co-ordination and consistency) and styles of problem-solving behaviour (functionally accurate, co-operative, nearly autonomous) within the architecture based on DPS. Characteristics the architecture and functional modules should exhibit if operating in real-time are described based on RTKBS. A real-time blackboard-based agent is described as a basis for experimenting with architectures. To allow the architecture and each functional module to guarantee responses to events within set deadlines (an example of a specification) a mechanism for trading quality of solution with processing time available is described. The technique involves a variation on the AO∗ search algorithm called Pruning AO∗ (PAO∗) which removes over-deadline routes from a directed graph of possible solutions. For illustration, an example architecture involving several such agents is applied to the task of sonar interpretation for an Autonomous Underwater Vehicle. Results are presented showing the performance of the architecture in various configurations in terms of quality and confidence of solutions, time available, problem decomposition, coherency, co-ordination and consistency.

A hybrid approach for robust diagnostics of cutting tools

A new multisensor based hybrid technique has been developed for robust diagnosis of cutting tools. The technique combines the concepts of pattern classification and real-time knowledge based systems (RTKBS) and draws upon their strengths; learning facility in the case of pattern classification and a higher level of reasoning in the case of RTKBS. It eliminates some of their major drawbacks: false alarms or delayed/lack of diagnosis in case of pattern classification and tedious knowledge base generation in case of RTKBS. It utilizes a dynamic distance classifier, developed upon a new separability criterion and a new definition of robust diagnosis for achieving these benefits. The promise of this technique has been proven concretely through an on-line diagnosis of drill wear. Its suitability for practical implementation is substantiated by the use of practical, inexpensive, machine-mounted sensors and low-cost delivery systems. >

Application methodology for REAKT1 systems

In this paper, we describe a development methodology for building real-time knowledge-based systems (RTKBS) using the REAKT toolkit. In RAM (REAKT Application Methodology), we attempt to combine elements from three separate streams of ideas: Knowledge based systems (KBS), real-time systems (RT), and object-orientation (OO). The concept of application frameworks for building and reusing real-time knowledge-based systems is proposed. The methodology is illustrated with examples from an alarm handling application at an oil refinery.

Temporal data representation and reasoning in REAKT

The ability of reasoning about temporal data, representing past, current and expected application states is an important function to be accomplished by Real-Time Knowledge-Based Systems (RTKBS). The application of Knowledge-based systems to real-time problems has to deal with dynamic time-constrained environments and assess two of the most important requirements in real-time systems: the ability to react rapidly to changes in the environment and the guarantee of a bound on the response time. This paper presents a temporal framework for reasoning about the future behaviour of a dynamic time-constrained problem. The proposed mechanism is integrated into a multiagent blackboard architecture and will provide a perspective of the temporal functionalities offered in the REAKT tool

Temporal Reasoning in REAKT: An Environment for Real-Time Knowledge-Based Systems

Temporal representation and reasoning, as the ability of reasoning about temporal data, representing past, current and expected application states, is an important function to be accomplished by Real-Time Knowledge-Based Systems RTKBS, since these systems are usually applied in dynamic time-dependent problem domains. However, this feature is not completely nor usually addressed in current RTKBS tools. In this paper, a RTKBS architecture is presented, with special emphasis on its temporal reasoning function, which is integrated in a RTKBS environment with a multi-agent blackboard architecture. Representation and management of temporal data, representing past, current and future problem states and reasoning processes within these contexts are detailed.

Reakt: real-time architecture for time-critical knowledge-based systems

Owing to the increasing complexity of traditional real-time systems, there is a growing interest in applying AI techniques, and in particular knowledge-based systems, in this domain. However, the time- and memory-consuming AI algorithms, with unpredictable or highly variable performances, are usually not compatible with the strict requirements of real-time systems. The paper describes a software environment well adapted to the development and delivery of real-time knowledge-based systems. This environment is built around a multi-agent, multitask real-time architecture based on the blackboard model. The main aspect of the developed architecture is its ability to provide guaranteed response times, through the use of various scheduling techniques, among which is progressive reasoning. >

190 Designing real-time knowledge-based systems with PERFECT

190 Designing real-time knowledge-based systems with PERFECT

Artificial intelligence and the supervision of bioprocesses (real-time knowledge-based systems and neural networks).

The ability to supervise and control a highly non-linear and time variant bioprocess is of considerable importance to the biotechnological industries which are continually striving to obtain higher yields and improved uniformity of production. Two AI methodologies aimed at contributing to the overall intelligent monitoring and control of bioprocess operations are discussed. The development and application of a real-time knowledge-based system to provide supervisory control of fed-batch bioprocesses is reviewed. The system performs sensor validation, fault detection and diagnosis and incorporates relevant expertise and experience drawn from both bioprocess engineering and control engineering domains. A complementary approach, that of artificial neural networks is also addressed. The development of neural network modelling tools for use in bioprocess state estimation and inferential control are reviewed. An attractive characteristic of neural networks is that with the appropriate topology any non-linear functional relationship can be modelled, hence significantly reducing model-process mismatch. Results from industrial applications are presented.

ASYNCHRONOUS PRODUCTION SYSTEM FOR CONTROL OF AN AUTONOMOUS MOBILE ROBOT IN REAL-TIME ENVIRONMENT

Abstract Autonomous robotic systems designed for hazardous environments require the development of onboard real-time knowledge-based systems capable of generating inference-driven responses to asynchronous, external stimuli. Building such real-time expert systems involves the integration of traditional-knowledge engineering methodologies with time-constrained response and control capabilities. To address such requirements, the Asynchronous Production System (APS), which is a rule-based inference engine capable of dynamic and rapid interaction with its environment, is presented. The APS uses a concurrent-event-driven execution mechanism and an external input-data structure to facilitate the monitoring and processing of real-time asynchronous information. The enhanced conflict-resolution strategies and rule-interruptibility features of the APS execution mechanism allows the APS to make efficient use of the onboard system resources. The implementation of the APS involves the development of a shared-memory, multiprocessor architecture operating in MIMD mode. This articles provides a description of the APS and presents a prototype APS-based expert system developed to handle external emergencies for the HERMIES HB (Hostile Environment Robotic Machine Intelligent Experiment Series) robot. The prototype expert system demonstrates APS' capabilities of handling multiple levels of emergencies and performing knowledge-based recovery after the asynchronous information is processed.

Designing Real-Time Knowledge Based Systems with Perfect

For real-time knowledge based systems (RTKBS) to become viable complements to traditional information systems the application of proven methodologies for system analysis and design is of utmost performance. However, these methodologies do not provide any support to knowledge engineers about issues that are related to the design of RTKBS, such as: What is the necessary knowledge for the RTKBS to perform a certain task? How can this knowledge be used by inference strategies? How should the knowledge model and the inference strategies be implemented, such that the resulting model is maintainable and meets all time requirements? Answers to these questions are also not provided by tools that are suitable for implementing a RTKBS, such as COGSYS or G2. In this paper we will show that PERFECT (Programming EnviRonment For Expertsystems Constrained in reasoning Time) does support knowledge engineers in answering these questions, and hence that it bridges the gap between the traditional analysis and design methodologies, and implementation tools for RTKBS.

Predicting and Improving Response-Times of Perfect-Models

PERFECT (Programming EnviRonment For Expertsystems Constrained in reasoning Time) aims at supporting knowledge engineers during the development of real-time knowledge based systems (RTKBS). It provides support at the knowledge level (Newell, 1981) in offering guidance in modeling complex industrial processes, and in supplying ways to use this knowledge model during problem solving. Apart from support in modeling, PERFECT also provides support at the level of (real-time) software engineering by means of an analyzer that predicts the response-time of the knowledge model at run-time. In case the response-times are not as required, the analyzer proposes an alternative structure for the knowledge model.

Designing real-time knowledge based systems with perfect

For real-time knowledge based systems (RTKBS) to become viable complements to traditional information systems the application of proven methodologies for system analysis and design is of utmost performance. However, these methodologies do not provide any support to knowledge engineers about issues that are related to the design of RTKBS, such as: What is the necessary knowledge for the RTKBS to perform a certain task? How can this knowledge be used by inference strategies? How should the knowledge model and the inference strategies be implemented, such that the resulting model is maintainable and meets all time requirements? Answers to these questions are also not provided by tools that are suitable for implementing a RTKBS, such as COGSYS or G2. In this paper we will show that PERFECT (Programming EnviRonment For Expertsystems Constrained in reasoning Time) does support knowledge engineers in answering these questions, and hence that it bridges the gap between the traditional analysis and design methodologies, and implementation tools for RTKBS.