scholarly journals The Reasonable and Conscious Understanding System of reality Under Uncertainty

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Systems ◽  
Fuzzy Inference ◽  
Intelligent System ◽  
Self Organized ◽  
Intelligent Interface ◽  
Knowledge Objects ◽  
Statistical Systems ◽  
Computational Intelligent ◽  
Self Learning

The modern autonomous Expert and Statistical Systems of Artificial Intelligence (AI) cannot continuously, independently and consciously think, learn and develop. This is happening because the models, methods and technologies of their processing in these systems cannot synchronously actualized (trained), function, independently, systemically, situationally, continuously, accurately and on their own in the conditions unpredictability, uncertainty of changing situations and lack of data, information and knowledge about the objects during the process of their continuous perception from the fuzzy environmental reality. Consequently, the need arises to create self-learning, self-developing and self-organized computational intelligent systems that continuously perceive and process changing data, information and knowledge in their changing, uncertainty and previously unknown situation in the surrounding reality. To solve the above problems and to create a system of General AI, we offer the new concept of creating a Computational Intelligent System of a Reasonable and Conscious Understanding of reality under uncertainty through of developed by us following models, methods and technologies of: a) perception the reality of environment, b) self-developing memory, c) situational control of data, information, knowledge, objects, models and processes, d) presentation, generalization and explanation of knowledge, e) fuzzy inference, f) decision making, g) reasoning and thinking, h) cognition, and h) Dialog Control in communication with human, robots and systems through of the intelligent interface, which integrating this functionality into a coherent Reasonable and Conscious Understanding System of reality Under Uncertainty.

Artificial intelligence as a moving force of improvement and innovative development in education and pedagogic

2019 ◽  
pp. 21-30
Author(s):  
M. G. Koliada ◽  
T. I. Bugayova
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Interaction Analysis ◽  
Educational Data Mining ◽  
Main Task ◽  
Intellectual System ◽  
Will Force ◽  
To Come ◽  
Artificial Intelligence Systems

The article discusses the history of the development of the problem of using artificial intelligence systems in education and pedagogic. Two directions of its development are shown: “Computational Pedagogic” and “Educational Data Mining”, in which poorly studied aspects of the internal mechanisms of functioning of artificial intelligence systems in this field of activity are revealed. The main task is a problem of interface of a kernel of the system with blocks of pedagogical and thematic databases, as well as with the blocks of pedagogical diagnostics of a student and a teacher. The role of the pedagogical diagnosis as evident reflection of the complex influence of factors and reasons is shown. It provides the intelligent system with operative and reliable information on how various reasons intertwine in the interaction, which of them are dangerous at present, where recession of characteristics of efficiency is planned. All components of the teaching and educational system are subject to diagnosis; without it, it is impossible to own any pedagogical situation optimum. The means in obtaining information about students, as well as the “mechanisms” of work of intelligent systems based on innovative ideas of advanced pedagogical experience in diagnostics of the professionalism of a teacher, are considered. Ways of realization of skill of the teacher on the basis of the ideas developed by the American scientists are shown. Among them, the approaches of researchers D. Rajonz and U. Bronfenbrenner who put at the forefront the teacher’s attitude towards students, their views, intellectual and emotional characteristics are allocated. An assessment of the teacher’s work according to N. Flanders’s system, in the form of the so-called “The Interaction Analysis”, through the mechanism of fixing such elements as: the verbal behavior of the teacher, events at the lesson and their sequence is also proposed. A system for assessing the professionalism of a teacher according to B. O. Smith and M. O. Meux is examined — through the study of the logic of teaching, using logical operations at the lesson. Samples of forms of external communication of the intellectual system with the learning environment are given. It is indicated that the conclusion of the found productive solutions can have the most acceptable and comfortable form both for students and for the teacher in the form of three approaches. The first shows that artificial intelligence in this area can be represented in the form of robotized being in the shape of a person; the second indicates that it is enough to confine oneself only to specially organized input-output systems for targeted transmission of effective methodological recommendations and instructions to both students and teachers; the third demonstrates that life will force one to come up with completely new hybrid forms of interaction between both sides in the form of interactive educational environments, to some extent resembling the educational spaces of virtual reality.

Prediction of International Stock Markets Based on Hybrid Intelligent Systems

2016 ◽  
pp. 110-124
Author(s):  
Salim Lahmiri
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Stock Market ◽  
Intelligent Systems ◽  
Fuzzy Inference ◽  
Intelligent System ◽  
Market Price ◽  
Hybrid Intelligent Systems ◽  
Out Of Sample ◽  
Artificial Neural Network Ann

This paper compares the accuracy of three hybrid intelligent systems in forecasting ten international stock market indices; namely the CAC40, DAX, FTSE, Hang Seng, KOSPI, NASDAQ, NIKKEI, S&P500, Taiwan stock market price index, and the Canadian TSE. In particular, genetic algorithms (GA) are used to optimize the topology and parameters of the adaptive time delay neural networks (ATNN) and the time delay neural networks (TDNN). The third intelligent system is the adaptive neuro-fuzzy inference system (ANFIS) that basically integrates fuzzy logic into the artificial neural network (ANN) to better model information and explain decision making process. Based on out-of-sample simulation results, it was found that contrary to the literature GA-TDNN significantly outperforms GA-ATDNN. In addition, ANFIS was found to be more effective in forecasting CAC40, FTSE, Hang Seng, NIKKEI, Taiwan, and TSE price level. In contrary, GA-TDNN and GA-ATDNN were found to be superior to ANFIS in predicting DAX, KOSPI, and NASDAQ future prices.

Ethical Behavior and Legal Regulations in Artificial Intelligence (Part One)

2021 ◽  
pp. 12-26
Author(s):  
Mandy Goram ◽  
Dirk Veiel
Keyword(s):  
Artificial Intelligence ◽  
Ethical Behavior ◽  
Intelligent Systems ◽  
Adaptive System ◽  
Intelligent System ◽  
Legal Regulations ◽  
Legal Requirements

Artificially intelligent systems should make users' lives easier and support them in complex decisions or even make these decisions completely autonomously. However, at the time of writing, the processes and decisions in an intelligent system are usually not transparent for users. They do not know which data are used, for which purpose, and with what consequences. There is simply a lack of transparency, which is important for trust in intelligent systems. Transparency and traceability of decisions is usually subordinated to performance and accuracy in AI development, or sometimes it plays no role at all. In this chapter, the authors describe what intelligent systems are and explain how users can be supported in specific situations using a context-based adaptive system. In this context, the authors describe the challenges and problems of intelligent systems in creating transparency for users and supporting their sovereignty. The authors then show which ethical and legal requirements intelligent systems have to meet and how existing approaches respond to them.

scholarly journals Sensing and Biosensing in the World of Autonomous Machines and Intelligent Systems

2021 ◽  
Vol 2 ◽  
Author(s):  
Osvaldo N. Oliveira ◽  
Maria Cristina F. Oliveira
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Train Station ◽  
The World ◽  
Autonomous Machines

In this paper we discuss how nanotech-based sensors and biosensors are providing the data for autonomous machines and intelligent systems, using two metaphors to exemplify the convergence between nanotechnology and artificial intelligence (AI). These are related to sensors to mimic the five human senses, and integration of data from varied sources and natures into an intelligent system to manage autonomous services, as in a train station.

Special Issue on ISIS 2009, Dong-A University, Busan, Korea

2010 ◽  
Vol 14 (5) ◽  
pp. 549-549
Author(s):  
John-Tark Lee ◽  
Gyei Kark Park
Keyword(s):  
Artificial Intelligence ◽  
International Symposium ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Unscented Kalman Filter ◽  
Fuzzy Theory ◽  
Capacitive Sensor ◽  
Theoretical Work ◽  
Editorial Staff ◽  
Special Issue

The 10th International Symposium on Advanced Intelligent Systems 2009 (ISIS2009) held on August 17-19, 2009, at the Bumin Campus of Dong-A University (http://www.donga.ac.kr/) in Busan, Korea, was sponsored by the Korean Institute of Intelligent System Society (KIIS) and cosponsored technically by the Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT) and the Taiwanese Association for Artificial Intelligence (TAAI). The international symposium focused on state-of-art accomplishments, innovations, and potential directions in intelligent systems. It also marked an epoch of innovation and the dissemination of research into many interesting fields. Its broad theme covered the latest in technical fields, including artificial intelligence, intelligent systems, Ambient Intelligence (AmI), bioinformatics, information technology, and their wide-ranging applications, from basic theoretical work to practical engineering applications. The 80 featured papers were presented by 120 participants. With so many papers submitted to JACIII, this special issue consists of just two strictly selected papers. The first, deals with emerging research trends in robotics, proposing a new trajectory generation using the univariate Dynamic Encoding Algorithm for Searches (uDEAS) in the turning of a biped walking robot. The second paper, presenting the latest findings in AmI, details a newly designed and implemented robust capacitive sensor with parasitic parameter modeling over a range of high 200 KHz frequencies based on an Unscented Kalman Filter (UKF) algorithm. I would like to thank Mr. Kunihiko Uchida, Mr. Shinya Wakai, Ms. Reiko Ohta, and Mr. Shinji Isokawa as editorial staff of Fuji Technology Press for editing these complex manuscripts into their final form. And I really thank to Prof. Kaoru Hirota, Editor-in-Chief of JACIII for inviting me to direct this special issue on ISIS

scholarly journals Belief Change without Compactness

2021 ◽  
Vol 24 (1) ◽  
Author(s):  
Jandson S Ribeiro
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Finite Subset ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Belief Change ◽  
Temporal Logics ◽  
Sensing Actions ◽  
Rational Behaviour ◽  
Do So

Dealing with dynamics is a vital problem in Artificial Intelligence (AI). An intelligent system should be able to perceive and interact with its environment to perform its tasks satisfactorily. To do so, it must sense external actions that might interfere with its tasks, demanding the agent to self-adapt to the environment dynamics. In AI, the field that studies how a rational agent should change its knowledge in order to respond to a new piece of information is known as Belief Change. It assumes that an agent’s knowledge is specified in an underlying logic that satisfies some properties including compactness: if an information is entailed by a set X of formulae, then this information should also be entailed by a finite subset of X. Several logics with applications in AI, however, do not respect this property. This is the case of many temporal logics such as LTL and CTL. Extending Belief Change to these logics would provide ways to devise self-adaptive intelligent systems that could respond to change in real time. This is a big challenge in AI areas such as planning, and reasoning with sensing actions. Extending belief change beyond the classical spectrum has been shown to be a tough challenge, and existing approaches usually put some constraints upon the system, which are either too restrictive or dispense some of the so desired rational behaviour an intelligent system should present. This is a summary of the thesis “Belief Change without Compactness” by Jandson S Ribeiro. The thesis extends Belief Change to accommodate non-compact logics, keeping the rationality criteria and without imposing extra constraints. We provide complete new semantic perspectives for Belief Change by extending to non-compact logics its three main pillars: the AGM paradigm, the KM paradigm and Non-monotonic Reasoning.

scholarly journals Intellectual systems in the management of medical technologies and quality of life

2021 ◽  
Vol 26 (1) ◽  
pp. 18-29
Author(s):  
S.O. Kizhaev ◽  
V.O. Petrenko ◽  
N.V. Mazur ◽  
V.V. Belitsky ◽  
А.V. Mazur ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Artificial Intelligence ◽  
Intelligent Systems ◽  
Information Technologies ◽  
Clinical Medicine ◽  
Intelligent System ◽  
Technological Processes ◽  
Intelligence System ◽  
Artificial Intelligence Systems

The article is devoted to the development and use of intelligent systems in the management of medical technological processes and health-related quality of life (HRQOL). The relevance of the work is due to the need for effective use of intelligent systems in healthcare. The purpose of this work is to study the possibilities and prospects of using information technologies and artificial intelligence systems in clinical medicine to improve the efficiency of providing medical care to the population. Information retrieval method; theoretical analysis of legislative and regulatory documents, literary sources, Internet resources, research results; spectral-dynamic and mathematical analysis of the current state and assessment of the quality of life of an individual using the artificial intelligence system "CME". The paper analyzes the development trends of information technologies and artificial intelligence systems, as well as the features of their use in medical technological processes. As an example, the technological capabilities of the intelligent system Complex Medical Expert are briefly described.

Prediction of International Stock Markets Based on Hybrid Intelligent Systems

2016 ◽  
pp. 1651-1667
Author(s):  
Salim Lahmiri
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Stock Market ◽  
Intelligent Systems ◽  
Fuzzy Inference ◽  
Intelligent System ◽  
Market Price ◽  
Hybrid Intelligent Systems ◽  
Out Of Sample ◽  
Artificial Neural Network Ann

This paper compares the accuracy of three hybrid intelligent systems in forecasting ten international stock market indices; namely the CAC40, DAX, FTSE, Hang Seng, KOSPI, NASDAQ, NIKKEI, S&P500, Taiwan stock market price index, and the Canadian TSE. In particular, genetic algorithms (GA) are used to optimize the topology and parameters of the adaptive time delay neural networks (ATNN) and the time delay neural networks (TDNN). The third intelligent system is the adaptive neuro-fuzzy inference system (ANFIS) that basically integrates fuzzy logic into the artificial neural network (ANN) to better model information and explain decision making process. Based on out-of-sample simulation results, it was found that contrary to the literature GA-TDNN significantly outperforms GA-ATDNN. In addition, ANFIS was found to be more effective in forecasting CAC40, FTSE, Hang Seng, NIKKEI, Taiwan, and TSE price level. In contrary, GA-TDNN and GA-ATDNN were found to be superior to ANFIS in predicting DAX, KOSPI, and NASDAQ future prices.

scholarly journals Can Artificial Intelligence Author Laws: A Perspective from Russia

2020 ◽  
Vol 13 (3) ◽  
pp. 286
Author(s):  
Roman Dremliuga ◽  
Alexey Koshel
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Systems ◽  
Policy Making ◽  
Public Involvement ◽  
Intelligent System ◽  
Digital Technologies ◽  
Digital Transformation ◽  
Russian Economy ◽  
Speed Up ◽  
Technical Sense

This article discusses the issue of the introduction of digital technologies into policy-making. The article describes several systems of policy-making in the Russian Federation. In addition, the article discusses the issue of the introduction of a new System of policy-making in the light of the digital transformation of the Russian economy. The paper analyzes the capacities of digital technologies, including artificial intelligence (AI), in the context of their application in policy-making. The authors conclude that there are prerequisites and opportunities for deeper automation of the policy-making. This can improve the quality of the bills, can increase public involvement in the policy-making process, and speed up the development and adoption of new regulations. An intelligent system can develop legislative bills that are of superior technical quality and are non-contradictory in the context of both national and international legal systems. Digitalization processes should naturally lead to changes in the mechanism of policy-making, which in turn should lead to its greater automation. Moreover, insufficient automation today can become an obstacle in the digital transformation of the Russian economy. The authors conclude that in the future it would be possible for intellectual systems to author bills. The general development of AI systems shows that given the parameters of the problem and given the circumstance when the machine would be able to detect a center of social tensions in the community, the intelligent system itself would be capable of making proposals in the field of legislative regulation. The application of intelligent systems in policy-making is not without its drawbacks. Such systems are not transparent in the legal and technical sense and can also transfer human beliefs into the texts of the regulations. These problems can be addressed through public scrutiny and the introduction of a licensing system, however even this would create a number of new practical challenges.

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