Реализация системы моделирования поведения интеллектуальных агентов
This paper addresses the question of existence of relationships between usage of contemporary marketing practices and profitability for companies operating on the Russian market. To address this issue, we utilize an artificial intelligence method that so far was barely present in marketing and management science. The paper is not only promoting a novel research method, it also establishes the relationships between profitability and specific sets of marketing practices. We show that the companies having negative profitability make use of a wide spectrum of marketing practices (with an exception of interactive marketing) and they do not prioritize any specific types of practices. In contrary, profitable companies intensively use interactive marketing and also combine it with IT-marketing and network marketing. This shows that successful companies focus on relationship marketing in a variety of its forms.
The article is based on the annual Perm All-Russian scientific and practical conference "Artificial intelligence in solving urgent social and economic problems of the XXI century" (http://www.permai.ru/files/26.05.2018.pdf). It is also a brief overview of the results of the Perm branch of the Scientific Council of the Russian Academy of Sciences on the methodology of artificial intelli- gence, as well as several departments of the Perm state University, Perm state humanitarian pedagogi- cal University, National research University Higher school of Economics, Perm state medical University. The review covers the works that develop and apply the methods of artificial intelligence in the classical sense, i.e., those methods that simulate human intellectual activity by simulating natural mechanisms.
These are expert systems, genetic algorithms, neural networks, fuzzy mathematics. The scientific priori- ty of Perm scientists in the development of theoretical foundations and practical applications of artificial intelligence is emphasized.
Keywords: Artificial intelligence, neural network, expert system, genetic algorithm, theory, prac- tice, modeling, forecasting, optimization, recognition, data processing, knowledge extraction.
Results of a research of a legal regime of the intellectual property items created by spontaneous programs (artificial intelligence or robots) are presented in article. The author reasons a conclusion according to which it is necessary to recognize the results received by artificial intelligence as the protected intellectual property items on which there is no copyright; each such object has to have automatically assigned identification number allowing to determine, first, by what artificial intelligence he is created to establish, secondly, the developer of the spontaneous program having the exclusive right not only to this program, but also to the object created by it.
The paper focues on the anthropocentric illusion that is common to modern discussions on artificial intelligence. The author concludes that the reality of technological development provokes the emergence of a radically new kind of intelligence, incomparable with human, and also inaccessible to the cognitive capabilities of human species. Marvin Minsky's theory of artificial intelligence is used as the base optics for such an analysis. The result of its application is a cluster of inhuman epistemologies discovered in the framework of the so-called nonhuman turn.
This book constitutes the proceedings of the 6th International Conference on Algorithms and Discrete Applied Mathematics, CALDAM 2020, held in Hyderabad, India, in February 2020. The 38 papers presented together with 2 invited talks in this volume were carefully reviewed and selected from 102 submissions. The papers are organized in topical sections on graph algorithms, graph theory, combinatorial optimization, distributed algorithms, combinatorial algorithms, and computational complexity.
We extend the existing framework of semi-implicit variational inference (SIVI) and introduce doubly semi-implicit variational inference (DSIVI), a way to perform variational inference and learning when both the approximate posterior and the prior distribution are semi-implicit. In other words, DSIVI performs inference in models where the prior and the posterior can be expressed as an intractable infinite mixture of some analytic density with a highly flexible implicit mixing distribution. We provide a sandwich bound on the evidence lower bound (ELBO) objective that can be made arbitrarily tight. Unlike discriminator-based and kernel-based approaches to implicit variational inference, DSIVI optimizes a proper lower bound on ELBO that is asymptotically exact. We evaluate DSIVI on a set of problems that benefit from implicit priors. In particular, we show that DSIVI gives rise to a simple modification of VampPrior, the current state-of-the-art prior for variational autoencoders, which improves its performance.
This book constitutes the refereed proceedings of the 11th International Conference on Intelligent Data Processing, IDP 2016, held in Barcelona, Spain, in October 2016.
The 11 revised full papers were carefully reviewed and selected from 52 submissions. The papers of this volume are organized in topical sections on machine learning theory with applications; intelligent data processing in life and social sciences; morphological and technological approaches to image analysis.
This book constitutes the refereed proceedings of the 12th International Andrei P. Ershov Informatics Conference, PSI 2019, held in Novosibirsk, Russia, in July 2019.
The 18 full papers and 3 short papers presented in this volume were carefully reviewed and selected from 70 submissions. The papers cover various topics related to the Mathematics of Computing, Information Systems, Formal Languages, dependable and fault-tolerant Systems and Network, Automata Theory, and much more.