Experience in developing and using intelligent graphical computer systems

2021 ◽  
pp. 26-41
Author(s):  
Vladimir Kuznetsov ◽  
Irina Chizhova
Keyword(s):  
Artificial Intelligence ◽  
Computer Systems ◽  
Knowledge Bases ◽  
Parametric Models ◽  
Evaluation Systems ◽  
Building Information ◽  
Graphical System ◽  
Use Of Knowledge ◽  
Ore District

The article discusses the conceptual, methodological and practical aspects of building information and analytical systems, including forecasting and evaluation systems based on artificial intelligence and the use of knowledge bases, as well as the implementation of these principles in the research version of the developed intellectual and graphical system based on parametric models of deposits and ore fields of the Rudno-Altai mineragenic zone, used to assess the prospects of ore fields of the Zmeinogorsky ore district.

scholarly journals Automated knowledge base management: A survey

2017 ◽  
Author(s):  
Jorge Martinez-Gil
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Knowledge Base ◽  
Computer Systems ◽  
Knowledge Bases ◽  
Significant Progress ◽  
Knowledge Source ◽  
Fundamental Challenge ◽  
Base Management ◽  
Automated Knowledge

A fundamental challenge in the intersection of Artificial Intelligence and Databases consists of developing methods to automatically manage Knowledge Bases which can serve as a knowledge source for computer systems trying to replicate the decision-making ability of human experts. Despite of most of the tasks involved in the building, exploitation and maintenance of KBs are far from being trivial, and significant progress has been made during the last years. However, there are still a number of challenges that remain open. In fact, there are some issues to be addressed in order to empirically prove the technology for systems of this kind to be mature and reliable.

A Decision Support System for Artificial Recharge Plant

1991 ◽  
Vol 24 (9) ◽  
pp. 331-342 ◽  
Author(s):  
C. Masciopinto ◽  
V. Palmisano ◽  
F. Tangorra ◽  
M. Vurro
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Artificial Recharge ◽  
Groundwater Resources ◽  
Treatment Plant ◽  
Knowledge Bases ◽  
Wastewater Discharge ◽  
Training Tool ◽  
Qualitative And Quantitative ◽  
Expert System Shell

The need for artificial recharge plants is the result of the qualitative and quantitative worsening of groundwater resources due to increased pumping and wastewater discharge. This paper described a system that uses artificial intelligence techniques for designing an artificial recharge plant. The system can be used as a training tool for new engineers, as well as an aid in the choices for expert engineers. The system is an application of an expert system shell running on a common p.c. machine. The model is made up of two knowledge bases, respectively denoted as Quantity artificial recharge and Quality artificial recharge. The former is related to the quantitative aspects, such as geology, climate and land availability, the latter to qualitative aspects, such as water use and treatment plant. Two case studies have been implemented in order to confirm the validity of this kind of systemic approach.

scholarly journals Artificial intelligence in the synthesis of creative solutions

10.31519/1404 ◽  
2019 ◽  
Author(s):  
Александр Андрейчиков ◽  
Aleksandr Andreychikov ◽  
Ольга Андрейчикова ◽  
Olga Andreichicova
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Present Article ◽  
High Efficiency ◽  
Knowledge Bases ◽  
Computing Technology ◽  
Creative Solutions ◽  
Computer Aided ◽  
Physical Effects ◽  
Technical Systems

Invention problem solving is connected to essential expenses of labour and time, which is spent on the procedures of search and ordering of necessary knowledge, on generation of probable vari-ants of projected systems, on the analysis of offered ideas and de-cisions and understanding perspectiveness of them. The present article outlines the results of the developments in the field of cre-ating computing technology of the synthesis of new engineering on the level of invention. The most attention is paid to problem of computer aided designing on initial stages, where synthesis of new on principal technical systems is carried out. Computer-aided con-struction of new technical system is based on using of data- and knowledge bases of physical effects and of technical decisions as well as different heuristic systematization procedures. The synthe-sis of principles of function of the technical new systems is carried out with using experts knowledge and requires the application of the artificial intelligence methods and the methods of the deci-sions making theory for invention's tasks. Considered approach has been used for synthesis of new technical systems of different functional purposes and had shown high efficiency in computer-aided construction.

scholarly journals Capabilities and Challenges Using Machine Learning in Tunnelling

2021 ◽  
Author(s):  
Thomas Marcher ◽  
Georg Erharter ◽  
Paul Unterlass
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Sharp Increase ◽  
Tunnel Construction ◽  
Learning Approaches ◽  
Building Information ◽  
Tunnel Design ◽  
Mass Classification ◽  
Design Analyses ◽  
Operational Processes

Digitalization changes the design and operational processes in tunnelling. The way of gathering geological data in the field of tunnelling, the methods of rock mass classification as well as the application of tunnel design analyses, tunnel construction processes and tunnel maintenance will be influenced by this digital transformation. The ongoing digitalization in tunnelling through applications like building information modelling and artificial intelligence, addressing a variety of difficult tasks, is moving forward. Increasing overall amounts of data (big data), combined with the ease to access strong computing powers, are leading to a sharp increase in the successful application of data analytics and techniques of artificial intelligence. Artificial Intelligence now arrives also in the fields of geotechnical engineering, tunnelling and engineering geology. The chapter focuses on the potential for machine learning methods – a branch of Artificial Intelligence - in tunnelling. Examples will show that training artificial neural networks in a supervised manner works and yields valuable information. Unsupervised machine learning approaches will be also discussed, where the final classification is not imposed upon the data, but learned from it. Finally, reinforcement learning seems to be trendsetting but not being in use for specific tunnel applications yet.

scholarly journals BIM AUTOMATION: ADVANCED MODELING GENERATIVE PROCESS FOR COMPLEX STRUCTURES

2017 ◽  
Vol IV-2/W2 ◽  
pp. 9-16 ◽  
Author(s):  
F. Banfi ◽  
S. Fai ◽  
R. Brumana
Keyword(s):  
Laser Scanning ◽  
Parametric Models ◽  
New Paradigm ◽  
Generative Process ◽  
Information Models ◽  
Historic Structures ◽  
Building Information ◽  
3D Elements ◽  
Scientific Results ◽  
Complex Forms

The new paradigm of the complexity of modern and historic structures, which are characterised by complex forms, morphological and typological variables, is one of the greatest challenges for building information modelling (BIM). Generation of complex parametric models needs new scientific knowledge concerning new digital technologies. These elements are helpful to store a vast quantity of information during the life cycle of buildings (LCB). The latest developments of parametric applications do not provide advanced tools, resulting in time-consuming work for the generation of models. This paper presents a method capable of processing and creating complex parametric Building Information Models (BIM) with Non-Uniform to NURBS) with multiple levels of details (Mixed and ReverseLoD) based on accurate 3D photogrammetric and laser scanning surveys. Complex 3D elements are converted into parametric BIM software and finite element applications (BIM to FEA) using specific exchange formats and new modelling tools. The proposed approach has been applied to different case studies: the BIM of modern structure for the courtyard of West Block on Parliament Hill in Ottawa (Ontario) and the BIM of Masegra Castel in Sondrio (Italy), encouraging the dissemination and interaction of scientific results without losing information during the generative process.

scholarly journals Artificial Intelligence in General

2019 ◽  
pp. 1-16
Keyword(s):  
Artificial Intelligence ◽  
Computer Aided Design ◽  
Intelligent Systems ◽  
Production Systems ◽  
Knowledge Bases ◽  
Computer Integrated Manufacturing ◽  
Current Status ◽  
Computer Aided ◽  
Control Computer ◽  
History Of

There are many kinds of uses for artificial intelligence (AI) in almost every field. AI is quite often used for control, computer aided design (CAD) and computer aided manufacturing (CAM), machine control, computer integrated manufacturing (CIM), production spot control, factory control, intelligent control, intelligent systems, deep learning, the cloud, knowledge bases, database, management, production systems, statistics, to assist sales forces, environment examination, agriculture, art, livings, daily life, etc. The present AI uses will be reexamined whether there is any matter to be considered further or not in AI research directions and their purposes behind the current status by looking at the history of AI development.

Fundamentals of creating MIVAR expert systems

2021 ◽  
Author(s):  
Oleg Varlamov
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Expert System ◽  
Expert Systems ◽  
Decision Support Systems ◽  
Intelligent Systems ◽  
Autonomous Robots ◽  
Support Systems ◽  
Knowledge Bases ◽  
Computer Engineering

Methodological and applied issues of the basics of creating knowledge bases and expert systems of logical artificial intelligence are considered. The software package "MIV Expert Systems Designer" (KESMI) Wi!Mi RAZUMATOR" (version 2.1), which is a convenient tool for the development of intelligent information systems. Examples of creating mivar expert systems and several laboratory works are given. The reader, having studied this tutorial, will be able to independently create expert systems based on KESMI. The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems. Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.

Mivar databases and rules

2021 ◽  
Author(s):  
Oleg Varlamov
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Expert System ◽  
Expert Systems ◽  
Decision Support Systems ◽  
Support Systems ◽  
Knowledge Bases ◽  
Data Models ◽  
Active Network ◽  
New Model

The multidimensional open epistemological active network MOGAN is the basis for the transition to a qualitatively new level of creating logical artificial intelligence. Mivar databases and rules became the foundation for the creation of MOGAN. The results of the analysis and generalization of data representation structures of various data models are presented: from relational to "Entity — Relationship" (ER-model). On the basis of this generalization, a new model of data and rules is created: the mivar information space "Thing-Property-Relation". The logic-computational processing of data in this new model of data and rules is shown, which has linear computational complexity relative to the number of rules. MOGAN is a development of Rule - Based Systems and allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format. An example of creating a mivar expert system for solving problems in the model area "Geometry"is given. Mivar databases and rules can be used to model cause-and-effect relationships in different subject areas and to create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with the transition to"Big Knowledge". The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems. Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.

Decision Making Modeled as a Theorem Proving Process

2012 ◽  
Vol 4 (3) ◽  
pp. 1-11
Author(s):  
Jacques Calmet ◽  
Marvin Oliver Schneider
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Information Technology ◽  
Multiagent Systems ◽  
Theorem Proving ◽  
Mathematical Knowledge ◽  
Knowledge Bases ◽  
Advanced Mathematics ◽  
Knowledge Communities ◽  
Computational Systems

The authors introduce a theoretical framework enabling to process decisions making along some of the lines and methodologies used to mechanize mathematics and more specifically to mechanize the proofs of theorems. An underlying goal of Decision Support Systems is to trust the decision that is designed. This is also the main goal of their framework. Indeed, the proof of a theorem is always trustworthy. By analogy, this implies that a decision validated through theorem proving methodologies brings trust. To reach such a goal the authors have to rely on a series of abstractions enabling to process all of the knowledge involved in decision making. They deal with an Agent Oriented Abstraction for Multiagent Systems, Object Mechanized Computational Systems, Abstraction Based Information Technology, Virtual Knowledge Communities, topological specification of knowledge bases using Logical Fibering. This approach considers some underlying hypothesis such that knowledge is at the heart of any decision making and that trust transcends the concept of belief. This introduces methodologies from Artificial Intelligence. Another overall goal is to build tools using advanced mathematics for users without specific mathematical knowledge.

Modelling Concepts for BIM

2010 ◽  
pp. 1-18 ◽  
Author(s):  
Sander van Nederveen ◽  
Reza Beheshti ◽  
Wim Gielingh
Keyword(s):  
Life Cycle ◽  
Boundary Conditions ◽  
Physical Reality ◽  
Parametric Models ◽  
Clear Distinction ◽  
Building Information Modelling ◽  
Building Information Model ◽  
Information Modelling ◽  
Building Information ◽  
Simple Technology

Building Information Modelling (BIM) is potentially a great technology for the expression of knowledge, supporting interoperability and communication throughout the life-cycle of a building. In fact, Building Information Modelling is not a simple technology. It requires a sound understanding of a number of abstract modelling concepts. Next to being a technology, BIM can also be regarded as a method for making a low or non-redundant (i.e. with every fact represented only once) model of an artefact that is sufficient to realize it as well as simulating it before it actually becomes physical reality. This chapter discusses the modelling concepts of BIM: what is Building Information Modelling, what is a Building Information Model and what are its rationale and objectives? A clear distinction will be made between (a) that what is being modelled, such as requirements, function, boundary conditions, building configuration, connectivity, shape, processes lifecycle aspects and discipline views, and (b) how it can be modelled, such as through parametric models, part libraries, nD models, various representations and presentations, including visualizations. Finally, there is a brief discussion of relevant methods and languages for information modelling, such as ISO 10303 (STEP, EXPRESS), BuildingSMART (IFC, IFD and IDM), process modelling and recent ontology-based approaches.

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