Knowledge In Intelligent Systems Research Articles

Data analysis using system modeling

Expert systems are increasingly being used to format safe operations. But the functions of expert systems can perform not only assistance in making decisions, but also analyze processes and help at various stages. These actions are possible when considering a system with fuzzy data. The work is devoted to solving the problem of fuzzy inference knowledge in intelligent systems based on the use of fuzzy logic. The scheme of construction of continuous logic, the computation of values of membership functions of linguistic variables of output knowledge. The proposed approach is based on the use of continuous logic, which allows for a more accurate representation of fuzzy data compared to traditional logic. The construction of the continuous logic scheme involves the use of fuzzy sets for both input and output variables, which are then used to compute the values of membership functions of linguistic variables of output knowledge. In this approach, the expert system is able to analyze processes and assist at various stages by making use of fuzzy inference knowledge. The fuzzy inference mechanism is based on the use of fuzzy logic, which allows for a more nuanced understanding of complex systems and processes. The expert system is able to analyze data from various sources and make informed recommendations based on the available information. Overall, the use of expert systems based on fuzzy logic is becoming increasingly popular in a variety of industries. By improving the accuracy of data analysis and decision-making, these systems can help to ensure safe and efficient operations.

Sup_Ont

A domain-independent conceptual model that aims to be highly reusable across specific domain applications is provided by upper-level ontologies which usually describe abstract concepts. In this paper, the authors proposed Sup_Ont, a fundamental upper ontology. In this ontology, the structure of the universe shows the concept of reality that is defined to have an existence which is known as truth. The devised super ontology and hence the domain ontologies can be reused across applications because of the generalized representation scheme used that is an EHCPR. An extended hierarchical censored production rules (EHCPRs) system is a knowledge representation system for reasoning with real-life problems and a step towards a generalized representation system. An EHCPR is a unit of knowledge resulting in a knowledge base that shows modularity and hierarchy. Extended hierarchical censored production rules (EHCPRs) have been used to represent the knowledge in intelligent systems.

An intelligent knowledge system for designing, modeling, and recognizing the behavior of elderly people in smart space

In this paper, a context-sensitive descriptive language is proposed to design and model the daily living activities of elderly people. The objective is to simplify and represent correctly the knowledge collected by sensors (low level) and to have a relevant recognition of the person’s knowledge (high level). The proposed language is based on several rules and constraints through intelligent meaning. It is dedicated to a better understanding and semantic design and description of the behavior of elderly people. Subsequently, in order to provide a powerful knowledge recognition system, a hybrid Markov model is proposed to recognize and predict the activities designed by the proposed language. The proposed model is adapted to the reasoning of the new language. This allows providing a hierarchical and temporal relationship within the knowledge. It is responsible to recognize and predict the behavior of the elderly people efficiently. The flexibility and the intelligibility of the proposed language is proven and the accuracy of the recognition model is demonstrated which ensures the efficiency of the proposed knowledge recognition system.

Distance Learning Systems and thei Development Using the Cognitive Mechanisms

The aim of the study is electronic learning systems. They refer to organizational and technical systems and use different approaches and technologies to solve learning problems. In modern conditions, education is becoming one of the main factors for the successful development of countries with the developed economies. Knowledge is beginning to occupy key positions in the life of these countries. Specialists, who have received professional education and want to improve their level of knowledge, are the key resource of the economy. Lifelong education has become a necessary and increasingly dominant element of modern educational systems. Modern distance learning systems are used to solve these problems. The paper deals with electronic distance learning systems and explores the possibility of using cognitive mechanisms for the development of educational technologies.Materials and methods. Ongoing education requires new approaches and technologies that fit well into distance learning systems. These systems use the following approaches and technologies: service-oriented architectures, cloud technologies and virtualization, intelligent dynamic systems, multi-agent systems, ontologies, evolving knowledge. The authors use machine learning technologies and agent-oriented approach to apply cognitive mechanisms. Integrated methods of knowledge representation are used to describe the reality. Using these approaches and methods, the authors consider the development and construction of modules of intelligent learning systems with the integration of computer paradigm and cognitive mechanisms.Results.The article presents an example of the development and use of educational resources for learning a foreign language in a technical University. The electronic course is created in the distance learning system Moodle. The result of testing students after studying the definite topic is shown. The article considers the structure of hardware and software modules for the formation of concepts from the sensual images of objects and phenomena of reality. Hardware-software modules are presented, which are necessary for the formation of concepts-representations from a variety of sensory mappings of control actions. A demo example of the formation of the concept scenarios and a fragment of the knowledge base containing the generated concept scenario are given.Conclusion.The use of distance learning system Moodle allows students to work out the current material of the course. This material can be worked out by students independently and repeatedly, till their full understanding and achievements of skill. Testing after each studied topic allows assessing the level of knowledge and the success of the current training of the definite course units. The considered approaches for the formation of concepts-representations and concepts-scenarios open up opportunities for the use of cognitive mechanisms. These approaches make it possible to use generalized knowledge in intelligent systems for the formation of new solutions for targeted behavior. Such approaches can be used both in training systems for the assimilation of new knowledge and in simulators of advanced systems for the formation of skills. Cognitive technologies can be used in social communities of agents.

Symbolic Approximate Reasoning with Fuzzy and Multi-valued Knowledge

Knowledge-based systems have nearly become omnipresent in various sectors to facilitate decision-making. Their aim is to get close to human induction. For that, dealing imprecise knowledge is essential since human thinks imprecisely. The principal logics that allow manipulating this kind of knowledge in intelligent systems are fuzzy logic and multi-valued logic. Up to now, according to our knowledge, knowledge-based systems manage separately either fuzzy knowledge or multi-valued knowledge. However, modeling heterogeneous knowledge (fuzzy and multi-valued) in the same inference engine should ensure more flexibility and freedom to the user. In that context, our aim is to allow the use of fuzzy and multi-valued knowledge at once. We propose a new approach to convert fuzzy knowledge into symbolic knowledge by projecting fuzzy inputs over the x-axis that corresponds to the universe of discourse of fuzzy variable. In order to demonstrate its applicability, our proposal is tested within a rule-based system. A numerical example is then provided.

Evaluating intelligent knowledge systems: experiences with a user-adaptive assistant agent

This article examines experiences in evaluating a user-adaptive personal assistant agent designed to assist a busy knowledge worker in time management. We examine the managerial and technical challenges of designing adequate evaluation and the tension of collecting adequate data without a fully functional, deployed system. The CALO project was a seminal multi-institution effort to develop a personalized cognitive assistant. It included a significant attempt to rigorously quantify learning capability, which this article discusses for the first time, and ultimately the project led to multiple spin-outs including Siri. Retrospection on negative and positive experiences over the 6 years of the project underscores best practice in evaluating user-adaptive systems. Lessons for knowledge system evaluation include: the interests of multiple stakeholders, early consideration of evaluation and deployment, layered evaluation at system and component levels, characteristics of technology and domains that determine the appropriateness of controlled evaluations, implications of ‘in-the-wild’ versus variations of ‘in-the-lab’ evaluation, and the effect of technology-enabled functionality and its impact upon existing tools and work practices. In the conclusion, we discuss—through the lessons illustrated from this case study of intelligent knowledge system evaluation—how development and infusion of innovative technology must be supported by adequate evaluation of its efficacy.

Knowledge network model with neurocognitive processing capabilities

The prime focus of scientists and the researchers in the field of intelligent systems is oriented to understand and replicate information processing functionalities of the human brain network. There have been continuous efforts to develop an intelligent knowledge system that incorporates the neuronal processes involved in cognition. In this paper the authors give some details of their unique work on the development of a knowledge system with information processing functionalities moving toward cognitive processing of the human brain, using intelligent links and nodes with processing capabilities. The existing knowledge systems connect only the pieces of information represented by nodes of a network and connect nodes using connectors, referred as edges. The edge is an attribute defining a relationship (e.g. isA, hasA) between the nodes. These edges lack cognitive properties, and the nodes lack functional processing to support efficient information transfer between nodes. The main objective of this paper is to provide an overview of the characteristics of a neurocognitive knowledge network model (NCKM) developed by the authors. NCKM is a knowledge network with nodes and links developed to provide methods that deal with the cognitive processes of the human brain, useful for efficient information processing. These cognitive processes provide self-directivity and learning within the network for intelligent knowledge retrieval. This work opens up a pathway to ingrain cognitive and neuronal characteristics for information processing into knowledge networks.

The hybrid intelligent systems design using grey systems theory

Purpose– The purpose of this paper is to discuss how tacit and explicit knowledge determine grey knowledge and how these are stimulated through interactions within networks, forming the grey hybrid intelligent systems (HISs). The feedback processes and mechanisms between internal and external knowledge determine the apparition of grey knowledge into an intelligent system (IS). The extension of ISs is determined by the ubiquity of the internet but, in our framework, the grey knowledge flows assure the viability and effectiveness of these systems.Design/methodology/approach– Some characteristics of the Hybrid Intelligent Knowledge Systems are put forward along with a series of models of hybrid computational intelligence architectures. More, relevant examples from the literature related to the hybrid systems architectures are presented, underlying their main advantages and disadvantages.Findings– Due to the lack of a common framework it remains often difficult to compare the various HISs conceptually and evaluate their performance comparatively. Different applications in different areas are needed for establishing the best combinations between models that are designed using grey, fuzzy, neural network, genetic, evolutionist and other methods. But all these systems are knowledge dependent, the main flow that is used in all parts of every kind of system being the knowledge. Grey knowledge is an important part of the real systems and the study of its proprieties using the methods and techniques of grey system theory remains an important direction of the researches.Originality/value– The paper discusses the differences among the three types of knowledge and how they and the grey systems theory can be used in different hybrid architectures.

A Research and Implementation of Internal Medicine Diagnosis Assisted by Intelligence Knowledge Base

Intelligent knowledge system is an important knowledge base for internal medicine diagnosis. Intelligent diagnosis of the knowledge base can be realized by establishing appropriate expert models to assist diagnosis and treatment. By building the hierarchical model of internal diseases, this paper established an internal medicine diagnostic system assisted by intelligence knowledge base with the mathematical model of analytic hierarchy. The hierarchical model is able to summarize characteristics of diseases and quantize the determinant criterion of diseases. The weighted value of a possible disease can be obtained through the judgment of physicians on the weight of factors of the criterion layer and the compared calculation of database. It is concluded that the analytic hierarchy model can realize the auxiliary diagnosis function of intelligence knowledge base and the weight of a disease providing diagnostic reference for physicians.

Epistemological foundations of the JSM method for automatic hypothesis generation

This article consists of two parts. Part I considers the so-called problem of induction, viz., the history of its genesis and elaboration, and formulates the main principles and logical tools of the JSM method for automatic hypothesis generation, including JSM reasoning. JSM reasoning is demonstrated to be a synthesis of three cognitive procedures: induction, analogy, and abduction. Two possible kinds of abduction are considered. Part I focuses on formal tools to be used for the solution of the natural-scientific problem of induction. Part II formulates the procedural semantics for predicates that are used to formalize JSM reasoning, considers quasi-axiomatic (open) theories used to represent knowledge in intelligent systems, determines the means for assessing the quality of JSM-reasoning results, and, finally, suggests a solution of the naturalscientific problem of induction for intelligent systems. Part I was published in the journal Nauchno-Tekhnicheskaya Informatsiya, ser. 2, 2013, no. 9, pp. 1---29. Part II was published in the journal Nauchno-Tekhnicheskaya Informatsiya, ser. 2, 2013, no. 12, pp. 1---26.

How Intelligent Knowledge Systems Will Improve Operations and Safety in the Future

Guest editorial In an era of high workforce turnover, capturing operational knowledge is vital to improving safety and achieving operational excellence. Modern solutions must address not only the replacement of paper-based processes, but also contextually weave together multidisciplinary information in a form that provides a holistic view of equipment across an operation. Going forward, the oil and gas industry should have intelligent, automated systems designed to capture data, learn from events, optimize production, and prevent safety-related incidents. The time is now for this step change in the industry as these fully integrated systems will become as necessary and as expected as the use of tablets and laptops. Trends and events in the oil and gas industry in recent years have shown that the focus on operation, maintenance, and safety requires more integration and embedding into operational processes. The industry has been aware of the issue of staff turnover for some time and the causes are not just aging baby boomers, but a change in working trends. The retention of employees is no longer assured through incentives, such as a good pension package, and the modern workforce recognizes that their skills are highly transportable. Lives are built more around portability, and information that would encourage one to realize new ambitions is more readily available than ever before. What attracted and retained previous generations of the workforce no longer has the same appeal. The industry must adapt to this new status quo and protect against the increasing risk that vital, safety-related information could disappear from an organization. With increased staff turnover, the risk that an organization might not keep required information is particularly high in activities that are not part of daily operations. Less frequent occurrences, such as an operation startup or coming out of a turnaround process, may leave the company particularly vulnerable. Information may be especially limited when it comes to taking long-life, rarely cycled equipment in and out of operation. However, it is under such less frequent conditions that accidents are more likely to occur. To combat the loss of knowledge retained by staff, intelligent systems should be not only work-process driven but also capture valuable information. These systems must be used by both seasoned and new employees and to capture and harness knowledge about various aspects of operations. They should learn, be intelligent, and make the knowledge they contain clearly available to operators as they work. In this way, when there are transitions, abnormal conditions, or opportunities for improvement, operators and engineers have ready access to the background and information gained from the past 20 years of accumulated knowledge. Such a system is a living, breathing application beyond anything that has been used in the industry to date.

A general knowledge mediation infrastructure for multi-agent systems

Intelligent decision making needs to be equipped with broader knowledge in order to enhance the decision quality. Knowledge for decision making can be categorized as domain specific and general. Applying domain knowledge in intelligent systems is not new, but applying general knowledge to support business decision making is a possible way to obtain an edge over competitors. For this reason, the paper focuses primarily on designing a general knowledge mediation infrastructure (GKMI) which supports the use of general knowledge from multiple heterogeneous sources, and provides an unified access point for typical multi-agent systems (MAS) to access that knowledge. The finite state automaton (FSA) is used to model and analyze the commonsense inference ability of GKMI. By carrying out two use cases of GKMI for MAS development and operation the effectiveness of this infrastructure is examined.

Ontology-Oriented SCJP Learning and Assessing System Design

In this paper, we proposed an advanced Petri Net model to manage the workflow of a web-based multiple participants in virtual University. The presented approach not only can conspicuously help the developer to comprehend the interaction relationship but also to easily construct a shared and trusted virtual world. This approach was based on the scaffolding theory and ontology. Learning activities of students and teachers are supported and understood by each other by the intelligent knowledge system. Their behaviors including the student’s self regulations and the teacher’s/virtual campus regulations are performed and built with trust development. Ontology based course content SCJPLearn can be supported in teaching activities and assessment stage. This paper can be taken as a fundamental research framework and tools to study and understand the characteristics of e-learning and to explore its optimal vocational education application.

Grounding commonsense knowledge in intelligent systems

Ambient environments which integrate a number of sensing devices and actuators intended for use by human users need to be able to express knowledge about objects, their functions and their properties to assist in the performance of everyday tasks. For this to occur perceptual data must be grounded to symbolic information that in its turn can be used in the communication with the human. For symbolic information to be meaningful it should be part of a rich knowledge base that includes an ontology of concepts and common sense. In this work we present an integration between ResearchCyc and an anchoring framework that mediates the connection between the perceptual information in an intelligent home environment and the reasoning system. Through simple dialogues we validate how objects placed in the home environment are grounded by a network of sensors and made available to a larger KB where reasoning is exploited. This first integration work is a step towards integrating the richness of a KRR system developed over many years in isolation, with a physically embedded intelligent system.

Intelligence Metasynthesis and Knowledge Processing in Intelligent Systems

Intelligence and Knowledge play more and more important roles in build- ing complex intelligent systems, for instance, intrusion detection systems, and oper- ational analysis systems. Knowledge processing in complex intelligent systems faces new challenges from the increased number of applications and environment, such as the requirements of representing domain and human knowledge in intelligent systems, and discovering actionable knowledge on a large scale in distributed web applications. In this paper, we discuss the main challenges of, and promising approaches to, intel- ligence metasynthesis and knowledge processing in open complex intelligent systems. We believe (1) ubiquitous intelligence, including data intelligence, domain intelligence, human intelligence, network intelligence and social intelligence, is necessary for OCIS, which needs to be meta-synthesized; and (2) knowledge processing should pay more attention to developing innovative and workable methodologies, techniques, tools and systems for representing, modelling, transforming, discovering and servicing the uncer- tain, large-scale, deep, distributed, domain-oriented, human-involved, and actionable knowledge highly expected in constructing open complex intelligent systems. To this end, the meta-synthesis of ubiquitous intelligence is an appropriate way in designing complex intelligent systems. To support intelligence meta-synthesis, m-interaction can play as the working mechanism to form m-spaces as problem-solving systems. In build- ing such m-spaces, advancement in knowledge processing is necessary.

Globalization Of Knowledge Discovery And Information Retrieval In Teaching And Learning

Developments in communication and information technologies in the last decade have had a significant impact on instructional and learning activities. For many students and educators, the Internet became the significant medium for sharing instruction, learning and communication. Access to knowledge beyond boundaries and cultures has an impact on methods of teaching and learning, affecting professional education in corporations worldwide. The blending of science with commercial applications, new methods of conducting business, and access to published and preliminary research is not longer limited to leading educational institutions and the northern hemisphere. This paper discusses educational opportunities, reviewing the latest trends in information retrieval, along with new teaching methodologies related to availability and globalization of information. The good news is that the multicultural melding of various intelligences and the global linking of intelligent knowledge systems promises to lend a multicultural wisdom to the selection and provision of information. We hope that this may ultimately contribute to a lessening of the gap between educationally rich and poor nations. In this paper the “digital divide” is seen to be a less vexing problem for the future of globalized intelligent information systems. This system, called the “Global Brain,” ameliorates existing differences in national access to educational content repositories used for teaching and learning.

A knowledge and analytical-based intelligent system for customising logistics management

In recent years, logistics service companies have been seeking numerous technologies and logistics information systems to support daily logistics operations. However, the planning and decision making of logistics activities is still done manually, where subjective judgment is made by Logistics Service Providers (LSP) through analysing multiple knowledge/information sources and various quantitative and qualitative criteria. In this paper, the use of an intelligent Decision Support System (DSS), namely, Knowledge and Analytical-based Intelligent System (KAIS), to embed the information communication and decision support technology in aiding such decision-making process is explained. KAIS is developed by integrating Analytic Hierarchy Process (AHP), Case-Based Reasoning (CBR) and portal technologies seamlessly, which is suitable for application in various dynamic logistics operations environment. By using KAIS in Eastern Logistics Limited (ELL), the overall logistics service quality and customer satisfaction level is significantly enhanced through making accurate decision and systematic planning of logistics operations.

Solving Mixed and Conditional Constraint Satisfaction Problems

Constraints are a powerful general paradigm for representing knowledge in intelligent systems. The standard constraint satisfaction paradigm involves variables over a discrete value domain and constraints which restrict the solutions to allowed value combinations. This standard paradigm is inapplicable to problems which are either: (a) mixed, involving both numeric and discrete variables, or (b) conditional,1 containing variables whose existence depends on the values chosen for other variables, or (c) both, conditional and mixed. We present a general formalism which handles both exceptions in an integral search framework. We solve conditional problems by analyzing dependencies between constraints that enable us to directly compute all possible configurations of the CSP rather than discovering them during search. For mixed problems, we present an enumeration scheme that integrates numeric variables with discrete ones in a single search process. Both techniques take advantage of enhanced propagation rule for numeric variables that results in tighter labelings than the algorithms commonly used. From real world examples in configuration and design, we identify several types of mixed constraints, i.e. constraints defined over numeric and discrete variables, and propose new propagation rules in order to take advantage of these constraints during problem solving.

Basic Science in Medical Reasoning: An Artificial Intelligence Approach.

A new generation of intelligent systems is growing up in the community of Artificial Intelligence in Medicine. The main goal of these systems is the representation and use of real theory of diseases, as they are represented in medical textbooks or in scientific articles, rather than the heuristic shortcuts of human experts. In this paper, we will argue that the difficulties in the integration of basic science and clinical knowledge in intelligent systems arise from ontological differences between these kinds of knowledge and that the solution can be found in their dynamic integration during the reasoning process. In order to illustrate this point, we will first describe an epistemological analysis of the interplay between basic science knowledge and clinical knowledge, and then we will provide the example of a computational architecture implementing this view.

Matching Intelligent Systems with Business Process Reengineering

According to Venkatraman (1991) five degrees of IT-induced business reconfiguration can be distinguished: (1) localized exploitation of IT, (2) internal integration, (3) business process redesign, (4) business network redesign, and (5) business scope redefinition. On each of these levels, different types of intelligent systems are applicable. On the level of localized exploitation of IT, the added value of standalone intelligent systems is limited. At the second level (i.e. the internal integration level), intelligent systems may play an important role, especially when they are integrated within information retrieval and messaging systems. On the last three levels, intelligent systems enable new business process solutions by means of ‘knowledge reallocation’. Knowledge reallocation is the process of separation of knowledge from its original resources by transporting it towards earlier stages of the business process. By packaging the knowledge in intelligent systems, the reallocated knowledge becomes usable in the earliest stages of the business process for non-specialized workers. Performance improvements caused by these systems are illustrated by examples in financial services organizations. © 1996 Wiley Periodicals, Inc.