Deductive logic in solving computer knowledge representation

Engineering design tasks are mainly knowledge-intensive cognitive actions. Therefore, an adequate computer-based structure and representation of engineering design knowledge is a crucial point to support the design engineer's cognitive tasks. On the one hand, this knowledge representation should correspond to an intuitive way of thinking. On the other hand, this representation should comply with conscious systematic design methods without limiting human problem solving. In a first step of an analytical research approach, this contribution is concerned with deriving detailed requirements on a useful knowledge representation for computer-aided engineering design. For that purpose, generalised characteristics of human problem solving and information processing can be specified for design problem solving. According to their fulfilment of these requirements, eight main models of knowledge representation were compared concerning their utility to support human problem solving in engineering design. As a result of this evaluation, a useful model of knowledge representation is suggested and illustrated by an example of use.

Knowledge representation is the core of artificial intelligence research. Knowledge representation methods include predicate logic, semantic network, computer programming language, database, mathematical model, graphics language, natural language, etc. To establish the intrinsic link between various knowledge representation methods, a unified knowledge representation model is necessary. According to ontology, system theory, and control theory, a standard model of knowledge representation that reflects the change of the objective world is proposed. The model is composed of input, processing, and output. This knowledge representation method is not a contradiction to the traditional knowledge representation method. It can express knowledge in terms of multivariate and multidimensional. It can also express process knowledge, and at the same time, it has a strong ability to solve problems. In addition, the standard model of knowledge representation provides a way to solve problems of non-precision and inconsistent knowledge.

Dual coding of knowledge in the human brain.

Cyber situational awareness is the result of both the analysis of cyber security and situational awareness studies and the line of research that uses artificial intelligence methods in the field of cybersecurity. It covers both methods of automatic detection of cyber threats in the network and methods of providing information to an analyst for further risk analysis and decision making to protect of the assets of the facility. Investigations of cyber situational awareness in the energy sector have become pertinent resulting from both the concept of the digital transformation of energy and the consideration of energy facilities and systems as cyber-physical systems. The problems of ensuring cybersecurity and raising awareness about the cyber environment of an energy facility are compounded by their high significance for the economies of countries. In this regard, such facilities are considered as critical infrastructure. The first part of this article discusses the basic concepts of cyber situational awareness, some knowledge representation models and some existing security metrics. The second part considers the use of frame, production and network models of knowledge representation in the analysis of the cyber situational awareness of energy facilities and the software components that implement them.

Knowledge Representation and Inference Engine Model of SAPS Gaming Concept

According to the diversity of geological conditions involved in railway location design, different types of geological objects were classified and indexed, and proposed the modeling method based on multiple sub-bases. It used the computer's internal and external representation in geological knowledge representation. In computer external, it used object-oriented class-rules knowledge representation model, added credibility to the object-oriented knowledge representation model, and then achieved fuzzy representation of knowledge. In order to achieve visual representation of external knowledge representation, text, images and three-dimensional virtual reality technology were used. According to the ambiguity of geological knowledge, it realized uncertainty reasoning of the part based on rule condition and conclusions.

Through used Fuzzy Petri Net to set up the knowledge representation and reasoning rules, a Fuzzy Neural Petri Network (FNPN) method whose parameters can be trained by Neural Network was proposed in this paper. The experimental result indicated that, the Neural Network and the Petri net combination can make the fuzzy production rule systems more effective, and also can be applied to most practical Petri net models and fuzzy systems.

In this paper we present a framework for unified, personalized access to heterogeneous multimedia content in distributed repositories. Focusing on semantic analysis of multimedia documents, metadata, user queries and user profiles, it contributes to the bridging of the gap between the semantic nature of user queries and raw multimedia documents. The proposed approach utilizes as input visual content analysis results, as well as analyzes and exploits associated textual annotation, in order to extract the underlying semantics, construct a semantic index and classify documents to topics, based on a unified knowledge and semantics representation model. It may then accept user queries, and, carrying out semantic interpretation and expansion, retrieve documents from the index and rank them according to user preferences, similarly to text retrieval. All processes are based on a novel semantic processing methodology, employing fuzzy algebra and principles of taxonomic knowledge representation. The first part of this work presented in this paper deals with data and knowledge models, manipulation of multimedia content annotations and semantic indexing, while the second part will continue on the use of the extracted semantic information for personalized retrieval.

Dynamic extraction of new knowledge and their further representation using a particular knowledge representation model are critically important tasks for most modern knowledge-based systems. To perform them a system should be able to generate appropriate knowledge structures, analyze and compare extracted knowledge with previously obtained ones, and then integrate new knowledge into the knowledge base in the proper way. Within the object-oriented approach, classes, objects, and relations among them are the main knowledge structures, consequently, these tasks can be done via using special operations defined over them, in particular, universal exploiters of classes and objects. Therefore we introduce in the paper concepts of a fuzzy inhomogeneous class of objects and universal union exploiter of fuzzy homogeneous classes of objects within such a knowledge representation model as fuzzy object-oriented dynamic networks. The proposed exploiter allows computing of the union of two fuzzy homogeneous classes of objects via the construction of a new fuzzy inhomogeneous class of objects, which simultaneously defines both fuzzy types determined by these classes, that allows avoiding of representation redundancy and provides effective integration of new classes into the knowledge base. We developed the algorithm, which implements the idea of the proposed universal exploiter, and provided examples of its application. The proposed exploiter can be adapted and integrated into a particular object-oriented knowledge representation model.KeywordsFuzzy classFuzzy typeUniversal union exploiterUnion of fuzzy classes

World experience shows that intelligent information systems for decision support are an integral part of modern society functioning. The basis of many tasks that lies in the development of decision support systems is the presentation of knowledge of a particular subject field. Each decision-support system, depending on the application field, has its own characteristics that characterize the main objectives of this subject area, among which, for example, the presentation of knowledge of multi-level administration systems for decision support purposes. In modern conditions, mathematical modeling is the most effective for the formal representation of knowledge in decision support systems which can be used to represent declarative knowledge of land relations. Therefore, urgent scientific practical problem is relevant now that lies in the representing contradictory knowledge in multilevel administration systems. The scientific and practical task envisages the transformation of declarative knowledge of the subject field in the form of mathematical and informational models using elements of set theory. The task envisages the transformation of declarative knowledge in the form of mathematical models with using set theory. The research used methods: analysis, set theory, mathematical modeling, corpus linguistics, ontological engineering. The results of the study are important for organizing activities in a variety of industries using a large body of documents and laws. The purpose of this work is to develop models of knowledge representation on the use of set theory in ontology. The object of research is the process of decision support in multilevel systems. Subject of research is the mathematical models of knowledge representation in multilevel administration systems using set theory. Keywords: formalization, theory, predicate logic, corpus linguistics, ontology, set theory, model.

A scheme for fuzzy temporal knowledge representation and reasoning is proposed. The scheme is based on the modification of the Petri nets called fuzzy time Petri nets and fuzzy temporal relations. The reasoning process uses modified Petri nets theory, possibility theory and a fuzzified version of Allen's (1983) temporal relations. The temporal linguistic expressions are modelled by primitives derived from the knowledge representation scheme. By combining and connecting these primitives the complex forms of situations from fuzzy temporal rich domains are modelled. An example of the use of the proposed scheme is given.

A set of fundamental cognitive processes of the brain is formally described in this paper. The cognitive processes defined at the meta cognitive level of the layered reference mode of the brain (LRMB) encompass those of <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">object identification, concept establishment, categorization, comparison, qualification, quantification, selection, </i> and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> search</i> . Real-time process algebra (RTPA) is adopted as the denotational mathematical means for rigorous modeling and describing the meta cognitive processes. The mathematical model of each meta process is created and modeled in RTPA. All cognitive models and processes are explained on the basis of the object- attribute-relation (OAR) model for internal information and knowledge representation and manipulation.

This article considers the ontological model for knowledge representation on the example of the discipline Database Theory.Depending on the chosen intelligent system development environment, knowledge must be represented by certain data structures. In this paper, the ontological model is chosen as a model for knowledge representation.

The goal of this paper is to establish that there exists a strong relationship between current issues of data models in Database Management and representations of knowledge in Artificial Intelligence. The different data modelling techniques that have been used in the two research areas are surveyed and classified into Predicate Calculus-based, Network, and Procedural ones. The similarities and differences between them are presented.

Sowa's conceptual structures work is broadly framed in terms of logic, linguistics, knowledge representation, conceptual analysis and mental models. Recently the CG representation has been proposed as a normative language for metalevel descriptions of traditional data models by the ANSI Information Resource Directory System (IRDS) working group (X3H4). This paper explores some issues that arise from attempts to use ontologies and “crystallized” representations to perform such things as data model translation and integration. Issues and alternate views that grow out of Sowa's knowledge soup metaphor are described and a functional architecture is proposed that addresses some of these issues. The notions of model knowledge as a dynamic and cognitive construction based on contexts is developed and combined with the idea of global workspaces.KeywordsData ModelConceptual SchemaConceptual StructureCognitive AgentFunctional ArchitectureThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.