Knowledgebased system

The more extended way of acquiring information for knowledge based systems is manually. However, the high cost of this approach and the availability of alternative Knowledge Sources has lead to an increasing use of automatic acquisition approaches. In this paper we present TURBIO, a Text-Based Intelligent System (TBIS) that extracts information contained in restricted-domain documents. The system acquires part of its knowledge about the structure of the documents and the way the information is presented (i.e. syntactic-semantic rules) from a training set of them. Then, a database is created by means of applying these syntactic-semantic rules to extract the information contained in the whole documents.

Most knowledge management (KM) projects aim at creating a knowledge base system in which all corporate knowledge is organized according to a single, supposedly shared and objective classi cation. The underlying assumption is that knowledge can be made objective re ning it of all its subjective, contextual, and social aspects. However, a lot of work in disciplines like arti cial intelligence, cognitive science, philosophy, linguistics, show that such an objectivistic epistemology is incompatible with the very nature of knowledge, and it is, therefore, one reason why KM systems are often deserted by users. Another approach, called Distributed Knowledge Management (DKM), is proposed in which subjective and social aspect are seriously token into account. Using this approach, we discuss a high level technological architecture, in which we introduce the idea of local classi cation, namely a classi cation created and maintained by a single organizational unit (e.g., a community or a division), which we call knowledge nodes (KNs). A system for DKM becomes a tool that supports two qualitatively different processes: the autonomous management of local classi cations within each knowledge node (principle of autonomy), and the coordination of the different KNs via a process of an agent-mediated meaning negotiation/coordination across different classi cations (principle of coordination).

BACKGROUND: With the rapid expansion of scientific research, the ability to effectively find or integrate new domain knowledge in the sciences is proving increasingly difficult. Efforts to improve and speed up scientific discovery are being explored on a number of fronts. However, much of this work is based on traditional search and retrieval approaches and the bibliographic citation presentation format remains unchanged. METHODS: Case study. RESULTS: The Telemakus KnowledgeBase System provides flexible new tools for creating knowledgebases to facilitate retrieval and review of scientific research reports. In formalizing the representation of the research methods and results of scientific reports, Telemakus offers a potential strategy to enhance the scientific discovery process. While other research has demonstrated that aggregating and analyzing research findings across domains augments knowledge discovery, the Telemakus system is unique in combining document surrogates with intera...

The recently initiated approach called computability logic is a formal theory of interactive computation. It understands computational problems as games played by a machine against the environment, and uses logical formalism to describe valid principles of computability, with formulas representing computational problems and logical operators standing for operations on computational problems. The concept of computability that lies under this approach is a nontrivial generalization of Church-Turing computability from simple, two-step (question/answer, input/output) problems to problems of arbitrary degrees of interactivity. Restricting this concept to predicates, which are understood as computational problems of zero degree of interactivity, yields exactly classical truth. This makes computability logic a generalization and refinement of classical logic. The foundational paper "Introduction to computability logic" [Annals of Pure and Applied Logic 123 (2003), pp. 1-99] was focused on semantics rather than syntax, and certain axiomatizability assertions in it were only stated as conjectures. The present contribution contains a verification of one of such conjectures: a soundness and completeness proof for the deductive system CL3 which axiomatizes the most basic first-order fragment of computability logic called the finite-depth, elementary-base fragment. CL3 is a conservative extention of classical predicate calculus in the language which, along with all of the (appropriately generalized) logical operators of classical logic, contains propositional connectives and quantifiers representing the so called choice operations. The atoms of this language are interpreted as elementary problems, i.e. predicates in the standard sense. Among the potential application areas for CL3 are the theory of interactive computation, constructive applied theories, knowledgebase systems, systems for resource-bound planning and action. This paper is self-contained as it reintroduces all relevant definitions as well as main motivations. It is meant for a wide audience and does not assume that the reader has specialized knowledge in any particular subarea of logic or computer science.

Artificial Neural Networks (ANNs) represents the overall interconnection of the systems together with numeric weighting that can be tuned based on experience, system Inputs, Processing and Outputs. Moreover, the real advantage of ANNs is the ability to solve complex system problems such as one which are found within the Transportation Infrastructure Systems. Artificial Neural Networks (ANNs) for Transportation Infrastructure System must incorporate system engineering techniques that will be sustainable for future years and maintained at acceptable levels. Accordingly, this paper will introduce the concept of Artificial Neural Networks (ANNs) and its core functions for the optimization of Transportation Infrastructure Systems in particular the maintenance processes.

This paper addresses broad issues relating the application of artificial intelligence (AI) technologies to the operation and control of large scale interconnected electric power systems. A fundamental issue discussed in this paper is the control structure of power systems. An evaluation of the control structure yields the characteristic requirements of knowledge-based systems and can indicate a means of integrating "intelligent" control with "algorithmic" control systems. As a result the nature of an overall knowledge-based philosophy can be speculated. The paper presents a hypothesis that the structure of knowledge, in a knowledge-based system, should evolve as the structure of the power system evolves in real-time. This hypothesis is explored in terms of the distributed and centralized power system control structures, and the hierarchies that exist in the control structures. The hierarchical nature of the control structures suggest a hierarchical structure in t...

Most knowledge management (KM) projects aim at creating a knowledge base system in which all corporate knowledge is organized according to a single, supposedly shared and objective classification. The underlying assumption is that knowledge can be made objective refining it of all its subjective, contextual, and social aspects. However, a lot of work in disciplines like artificial intelligence, cognitive science, philosophy, linguistics, show that such an objectivistic epistemology is incompatible with the very nature of knowledge, and it is, therefore, one reason why KM systems are often deserted by users. Another approach, called Distributed Knowledge Management (DKM), is proposed in which subjective and social aspect are seriously token into account. Using this approach, we discuss a high level technological architecture, in which we introduce the idea of local classification, namely a classification created and maintained by a single organizational unit (e.g., a community or a division), which we call knowledge nodes (KNs). A system for DKM becomes a tool that supports two qualitatively different processes: the autonomous management of local classifications within each knowledge node (principle of autonomy), and the coordination of the different KNs via a process of an agent-mediated meaning negotiation/coordination across different classifications (principle of coordination).

Artificial Neural Networks (ANNs) represents the overall interconnection of the systems together with numeric weighting that can be tuned based on experience, system Inputs, Processing and Outputs. Moreover, the real advantage of ANNs is the ability to solve complex system problems such as one which are found within the Transportation Infrastructure Systems. Artificial Neural Networks (ANNs) for Transportation Infrastructure System must incorporate system engineering techniques that will be sustainable for future years and maintained at acceptable levels. Accordingly, this paper will introduce the concept of Artificial Neural Networks (ANNs) and its core functions for the optimization of Transportation Infrastructure Systems in particular the maintenance processes.

A description of an Intelligent, Knowledge-Based maintenance tool, being developed by the Centre for Software Maintenance at the University of Durham is described. The tool is intended to help reduce the amount of time spent on analysing code. Code analysis is performed when a programmer is familiarising himself with a piece of code, and when the eoeects of a proposed modication of the code is being assessed. 1 The Problem There has been much research in recent years on the problems of program and system development, but very little work has been done on the problems of maintaining developed programs. The early promises of researchers in formal methods and structured programming, that the maintenance problem would isimply disappearj as their methods are developed and applied, have not been realised for several fundamental reasons: ffl There exists a large quantity of what might be termed as igeriatric codej. This code has often been heavily modied to make it perform many more fu...

Most knowledge management (KM) projects aim at creating a knowledge base system in which all corporate knowledge is organized according to a single, supposedly shared and objective classification. The underlying assumption is that knowledge can be made objective refining it of all its subjective, contextual, and social aspects. However, a lot of work in disciplines like artificial intelligence, cognitive science, philosophy, linguistics, show that such an objectivistic epistemology is incompatible with the very nature of knowledge, and it is, therefore, one reason why KM systems are often deserted by users. Another approach, called Distributed Knowledge Management (DKM), is proposed in which subjective and social aspect are seriously token into account. Using this approach, we discuss a high level technological architecture, in which we introduce the idea of local classification, namely a classification created and maintained by a single organizational unit (e.g., a community or a division), which we call knowledge nodes (KNs). A system for DKM becomes a tool that supports two qualitatively different processes: the autonomous management of local classifications within each knowledge node (principle of autonomy), and the coordination of the different KNs via a process of an agent-mediated meaning negotiation/coordination across different classifications (principle of coordination).

A description of an Intelligent, Knowledge-Based maintenance tool, being developed by the Centre for Software Maintenance at the University of Durham is described. The tool is intended to help reduce the amount of time spent on analysing code. Code analysis is performed when a programmer is familiarising himself with a piece of code, and when the e ects of a proposed modi cation of the code is being assessed.

A description of an Intelligent, Knowledge-Based maintenance tool, being developed by the Centre for Software Maintenance at the University of Durham is described. The tool is intended to help reduce the amount of time spent on analysing code. Code analysis is performed when a programmer is familiarising himself with a piece of code, and when the e ects of a proposed modi cation of the code is being assessed.

Background: The field of software development is not shy of presenting new procedures. For sure, over the most recent 25 years, various ways to deal with software development have been presented, of which just few are being adopted in present times. Agile Methodologies were introduced to meets the new requirements of the software development. Software engineers are confronting expanded strain to bring down time spent in developing software, provide updates and new versions of existing software and maintain their pace with the fastly changing trends in market. This new context leads engineers and organizations to move from an arrangement based cascade development cycle to an adaptable agile cycle. To create secure software, numerous organizations use security designing cycles that are plan weighty and unbendable. Review Aim: The goal of the paper is to audit how to create secure software in an agile cycle. What existing prescribed procedures can be fused into an agile extend and still give a similar advantage if the task was utilizing a cascade cycle. How the prescribed procedures can be consolidated and adjusted to fit the cycle while as yet estimating the improvement. Outcomes: The base agile and security versatility was assessed in efficient writing, by analysts and specialists. The security designing accepted procedures were bunch dependent on their motivation and their similarity with the agile cycle

Artificial Intelligence (AI) has developed at a rapid pace and this provides a good chance to enhance the execution of different fields like business, industries, and the transportation sector. In the transportation field, the AI had applied to overcome the challenges of pollution, environmental deterioration, increasing travel demand, and safety concerns. The good understanding of the relationship between the AI and input data on one hand and characteristics of the transportation system leads to a perfect and successful application of AI. This paper provides an overview of the AI methods with a concentration in two methods of AI, Knowledge Base System method (KBS) and Artificial Neural network systems (ANNs). The paper tried to explain in detail the two methods and their application in transportation with advice and points of strength, weakness, and guidelines for application. The second part of the paper deal with AI application areas in Transportation. The overview concludes by a summary of the two methods with a brief of their important application in transportation.