scholarly journals Artificial intelligence in the synthesis of creative solutions

10.31519/1404 ◽  
2019 ◽  
Author(s):  
Александр Андрейчиков ◽  
Aleksandr Andreychikov ◽  
Ольга Андрейчикова ◽  
Olga Andreichicova

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.

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.


2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.


2021 ◽  
Vol 14 ◽  
pp. 263177452199305
Author(s):  
Hemant Goyal ◽  
Rupinder Mann ◽  
Zainab Gandhi ◽  
Abhilash Perisetti ◽  
Zhongheng Zhang ◽  
...  

The role of artificial intelligence and its applications has been increasing at a rapid pace in the field of gastroenterology. The application of artificial intelligence in gastroenterology ranges from colon cancer screening and characterization of dysplastic and neoplastic polyps to the endoscopic ultrasonographic evaluation of pancreatic diseases. Artificial intelligence has been found to be useful in the evaluation and enhancement of the quality measure for endoscopic retrograde cholangiopancreatography. Similarly, artificial intelligence techniques like artificial neural networks and faster region-based convolution network are showing promising results in early and accurate diagnosis of pancreatic cancer and its differentiation from chronic pancreatitis. Other artificial intelligence techniques like radiomics-based computer-aided diagnosis systems could help to differentiate between various types of cystic pancreatic lesions. Artificial intelligence and computer-aided systems also showing promising results in the diagnosis of cholangiocarcinoma and the prediction of choledocholithiasis. In this review, we discuss the role of artificial intelligence in establishing diagnosis, prognosis, predicting response to treatment, and guiding therapeutics in the pancreaticobiliary system.


2002 ◽  
Vol 1 (1) ◽  
pp. 125-143 ◽  
Author(s):  
Rolf Pfeifer

Artificial intelligence is by its very nature synthetic, its motto is “Understanding by building”. In the early days of artificial intelligence the focus was on abstract thinking and problem solving. These phenomena could be naturally mapped onto algorithms, which is why originally AI was considered to be part of computer science and the tool was computer programming. Over time, it turned out that this view was too limited to understand natural forms of intelligence and that embodiment must be taken into account. As a consequence the focus changed to systems that are able to autonomously interact with their environment and the main tool became the robot. The “developmental robotics” approach incorporates the major implications of embodiment with regard to what has been and can potentially be learned about human cognition by employing robots as cognitive tools. The use of “robots as cognitive tools” is illustrated in a number of case studies by discussing the major implications of embodiment, which are of a dynamical and information theoretic nature.


Author(s):  
А.И. Гайкович ◽  
С.И. Лукин ◽  
О.Я. Тимофеев

Процесс создания проекта судна или корабля рассматривается как преобразование информации, содержащейся в техническом задании на проектирование, нормативных документах и знаниях проектанта, в информацию, объем которой позволяет реализовать проект. Проектирование может быть представлено как поиск решения в пространстве задач. Построение цепочки последовательно решаемых задач составляет методику проектирования. Проектные задачи могут быть разбиты на две группы. Первая группа ‒ это полностью формализуемые задачи, для решения которых есть известные алгоритмы. Например, построение теоретического чертежа по известным главным размерениям и коэффициентам формы. Ко второй группе задач можно отнести трудно формализуемые или неформализуемые задачи. Например, к задачам этого типа можно отнести разработку общего расположения корабля. Важнейшим инструментом проектирования современного корабля или судна является система ав­томатизированного проектирования (САПР). Решение САПР задач первой группы не представляет проблемы. Введение в состав САПР задач второй группы подразумевает разработку специального ма­тематического аппарата, базой для которого, которым является искусственный интеллект, использующий теорию нечетких множеств. Однако, настройка искусственных нейронных сетей, создание шкал для функций принадлежности элементов нечетких множеств и функций предпочтений лица принимающего решения, требует участие человека. Таким образом, указанные элементы искусственного интеллекта фиксируют качества проек­танта как специалиста и создают его виртуальный портрет. The process of design a project of a ship is considered as the transformation of information contained in the design specification, regulatory documents and the designer's knowledge into information, the volume of which allows the project to be implemented. Designing can be represented as a search for a solution in the space of problems. The construction of a chain of sequentially solved tasks constitutes the design methodology. Design problems can be divided into two groups. The first group is completely formalizable tasks, for the solution of which there are known algorithms. For example, the construction of ship's surface by known main dimensions and shape coefficients. Tasks of the second group may in­clude those which are difficult to formalize or non-formalizable. For example, tasks of this type can include develop­ment of general arrangement of a ship. The most important design tool of a modern ship or vessel is a computer-aided design system (CAD). The solu­tion of CAD problems of the first group is not a problem. Introduction of tasks of the second group into CAD implies development of a special mathematical apparatus, the basis for which is artificial intelligence, which uses the theory of fuzzy sets. However, the adjustment of artificial neural networks, the creation of scales for membership functions of fuzzy sets elements and functions of preferences of decision maker, requires human participation. Thus, the above elements of artificial intelligence fix the qualities of the designer as a specialist and create his virtual portrait.


2021 ◽  
Vol 26 (jai2021.26(1)) ◽  
pp. 95-101
Author(s):  
Pisarenko V ◽  
◽  
Pisarenko J ◽  
Gulchak O ◽  
Chobotok T ◽  
...  

The practical experience of solving scientific tasks using artificial intelligence technologies is presented. The authors offered their understanding of the term "artificial intelligence". Describes the development of the dept. №265 of Mathematical Problems of Applied Informatics V.M. Glushkov Institute of Cybernetics of the NAS of Ukraine in the creation of technical systems with elements of AI mainly to work in extreme environments. The purpose of the authors is to provide useful information to develop a strategy for the development of AI in the Ukraine. Some of these studies: monitoring the territory and management of land use technologies using remote sensing technologies from aircraft, spacecraft, unmanned aerial vehicles; monitoring the technical equipment of the underwater environment (technical means of searching for a sunken object of the submarine type for emergency operations are being developed); mine safety control (risk research during mining, creating robotic systems with elements of artificial intelligence for studying the conditions of work in the mine, warning accidents and emergency rescue work). The next direction is the diagnosis and treatment of addictive patients using the principles of therapeutic methods BiofeedBack. Attention is paid to the development of robotic technical systems with AI for servicing cosmic long missions. For this, theoretical studies have been conducted on the creation of a live brain mathematical model for its use in the development of the "artificial brain" of robots. The authors gave a list of tasks that can solve AI in programs for long-term space flights, technologies and systems that should develop in the first place to implement these tasks


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