A theorem-proving approach to spatial problem-solving

Session C3: AI architecture and applications I

The relationship between TMS and general logic programs is an important issue in non-monotonic logic programming. In this paper, we prove that, after we translate the TMS theory into a general logic program, the TMS's well-founded assignment (or extension) is equivalent to the corresponding general logic program's stable model. It means that TMS can be completely integrated into a non-monotonic logic programming environment.

Three-valued completion for abductive logic programs

We study revision programming, a logic-based mechanism for enforcing constraints on databases. The central concept of this approach is that of a justified revision based on a revision program. We show that for any program P and for any pair of initial databases I and I’ we can transform (shift) the program P to a program P’ so that the size of the resulting program does not increase and so that P-justified revisions of I are shifted to P’-justified revisions of I’. Using this result we show that revision programming is closely related to a subsystem of general logic programming of Lifschitz and Woo. This, in turn, allows us to reduce revision programming to logic programming extended by the concept of a constraint with a suitably modified stable model semantics. Finally, we use the connection between revision programming and general logic programming to introduce a disjunctive version of our formalism.

The presented article discusses the social impact of ‎innovations in the field of ICT and artificial ‎intelligence on society. The article examines the ‎possibilities of artificial intelligence's impact on the ‎social character of society, analyzes artificial ‎intelligence from its positive and negative aspects, and ‎notes the possibilities of pragmatic use of artificial ‎intelligence against the background of new changes, as ‎well as its real and probable consequences. At the same ‎time, it discusses global and regional challenges related ‎to artificial intelligence and analyzes the main goals of ‎national strategies related to artificial intelligence. ‎Preventive security measures and ethical aspects during ‎the use of artificial intelligence against the background ‎of changes in the field of artificial intelligence in ‎Azerbaijan are also among the main research topics.

Aim. The work aimed to identify the specific features of cross-border business development in the field of generative artificial intelligence (AI) and develop recommendations for the formation of promising strategies for Russian companies and the Government of the Russian Federation (RF). Objectives. The work seeks to identify the characteristic features of cross-border business and conduct a foresight session on its development prospects in the field of generative AI; to assess the main development trends of international business in the field of generative AI; to determine the specific features of Russian business development in the field of generative AI; to propose conceptual directions for a strategy to support cross-border business in the field of generative AI in Russia. Methods. The research methods include socioeconomic foresight using the “Four Worlds” technique, an expert survey, a literature review, conceptual and statistical analysis, and data extrapolation. Results. The work presents a scientific definition of cross-border business in the field of generative AI and characterizes organizational models for businesses using generative AI, commonly used in the modern context. The reasons for the steady transnationalization of generative AI business are substantiated, and its characteristics are highlighted. Approaches to and challenges in assessing the economic prospects for the development of international generative AI business are examined in detail, and the results of a foresight session on cross-border business development in generative AI are presented. The work revealed the potential of cross-border generative AI business originating in Russia, and identified the barriers to its development and opportunities for overcoming them, which are traced in the intensification of strategic interaction between the state and business. Conclusions. The most probable scenario for the development of cross-border online business in the field of generative AI is a combination of technological integration and competition among major players. For Russian companies and the Russian government, the key challenge in this regard is overcoming external obstacles and creating conditions for integrating own technologies into global value chains.

The work done by some authors in the fields of computer science, artificial intelligence, and multi-agent systems foresees an approximation of these disciplines and those of the social sciences, namely, in the areas of anthropology, sociology, and psychology. Much of this work has been done in terms of the humanization of the behavior of virtual entities by expressing human-like feelings and emotions. Some authors (e.g., Ortony, Clore & Collins, 1988; Picard, 1997) suggest lines of action considering ways to assign emotions to machines. Attitudes like cooperation, competition, socialization, and trust are explored in many different areas (Arthur, 1994; Challet & Zhang, 1998; Novais et al., 2004). Other authors (e.g., Bazzan et al., 2000; Castelfranchi, Rosis & Falcone, 1997) recognize the importance of modeling virtual entity mental states in an anthropopathic way. Indeed, an important motivation to the development of this project comes from the author’s work with artificial intelligence in the area of knowledge representation and reasoning, in terms of an extension to the language of logic programming, that is, the Extended Logic Programming (Alferes, Pereira & Przymusinski, 1998; Neves, 1984). On the other hand, the use of null values to deal with imperfect knowledge (Gelfond, 1994; Traylor & Gelfond, 1993) and the enforcement of exceptions to characterize the behavior of intelligent systems (Analide, 2004) is another justification for the adoption of these formalisms in this knowledge arena. Knowledge representation, as a way to describe the real world based on mechanical, logical, or other means, will always be a function of the systems ability to describe the existent knowledge and their associated reasoning mechanisms. Indeed, in the conception of a knowledge representation system, it must be taken into attention different instances of knowledge.

Logic Programming (LP) is an essential part of many academic curricula and it is extensively employed in the field of Artificial Intelligence. However, being based on a fundamentally different paradigm and lacking any visual tools for inexperienced users, its teaching may lead to confusion and low student motivation. Virtual Worlds (VWs) may help overcome these obstacles, as they have been successfully used in computer programming education. In this paper we present the MeLoISE platform (Meaningful Logical Interpretations of Simulated Environments) for teaching LP in VWs, through which students can experience a collaborative visual interface to the Prolog programming language. We also present an evaluation of MeLoISE, which was conducted during a learning activity that engaged students in collaborative programming for 2 problem scenarios. Students performed very well and were enthusiastic with the new environment.

This paper draws objective conclusions from field research, establishes a talent cultivation and technological innovation system in the field of artificial intelligence, analyzes the phenomena and problems that arise in school enterprise cooperation, explores their underlying reasons, and explores how to establish a talent cultivation mechanism for school enterprise cooperation in the field of artificial intelligence, establishes a basic model for artificial intelligence talent cultivation, establishes standards, human resources support, improves guarantee systems, reforms in teaching and learning, and integrates disciplines.

In this era of rapid development of global science and technology, artificial intelligence and industrial technology are converging at an unprecedented speed, driving social progress and industrial upgrading. In order to deeply discuss the latest development in these two and relevant fields, share research results, and promote academic exchanges and cooperation, we are honored to have organized the 2024 4th International Conference on Artificial Intelligence and Industrial Technology Applications (AIITA 2024). This proceedings is the crystallization of this event, which brings together the wisdom and insights of experts, scholars, engineers and technicians from all over the world. AIITA 2024 was grandly held in Guangzhou, China from April 12th to 14th, 2024 via hybrid form, attracting over 200 participants from all over the world. The conference aimed to provide a platform for experts and scholars, engineers and technicians, researchers and developers in the fields of artificial intelligence and industrial engineering to share scientific research results and cutting-edge technologies, understand academic development trends, broaden research ideas, and strengthen academic research and discussion. During the conference, the participants had an in-depth discussion on a number of topics related to the application of artificial intelligence and industrial technology, and jointly explored the new direction of future development. In the keynote speech part, latest research results and cutting-edge technologies in related fields including Graph Neural Networks: Models and Applications in Pattern Recognition (by Prof. Bo Jiang, Anhui University, China), Distributed GPU-accelerated Evolutionary Computation (by Dr. Ran Cheng, Southern University of Science and Technology, China), Quantum Model Learning (by Prof. Daowen Qiu, Sun Yat-sen University, China), Face Image Editing from the Perspective of Conditional Generative (by Prof. Si Wu, South China University of Technology, China), Cooperative Global Robust Output Regulation for Nonlinear Multi-Agent Systems with Switching Networks (by Prof. Wei Liu, South China University of Technology, China) were discussed. At the same time, we have set up a number of presentations and panel discussion sessions, in which participants gained more opportunities to have in-depth discussions and exchanges on specific issues. This conference calls for papers on a wide range of topics, covering all aspects of artificial intelligence and industrial technology applications. We received hundreds of submissions from all over the world, and after strict evaluation and screening, we finally included some excellent papers in this proceedings. These papers not only have high academic value and practical significance, but also represent the latest research dynamics and trends in the fields of artificial intelligence and industrial technology applications. Finally, we would like to thank all the experts, scholars, engineers and staff who participated in the AIITA 2024 for their support and contribution. We believe that with the joint efforts of all of us, the research and application in the fields of artificial intelligence and industrial technology applications will achieve more brilliant achievements. The Committee of AIITA 2024 List of Committee Member is available in this Pdf.

In modern society, artificial intelligence (AI) is developing more rapidly. And the Field Programmable Gate Array (FPGA) has always been the focus of research as a driving platform. This paper studies in detail the theoretical basis, applications, defects, and future development directions of FPGAs. It is concluded that FPGA has three characteristics: gate array, programmable, and scene, and the detailed positioning of FPGA, the structure, principle, tools, process, and description language of FPGA design. And the unique advantages of FPGA in the field of artificial intelligence: flexible and configurable, special optimizations for convolutional neural networks, and deterministic low latency. Several typical applications of FPGA in the field of artificial intelligence, deficiencies and solutions, and two future development directions. This article will make a great contribution to the development of FPGA in the field of artificial intelligence in the future.

Training personnel for the development of artificial intelligence technologies is one of the strategic tasks facing Russian universities. This article aims to analyze the employment of graduates from Russian universities in educational programs related to artificial intelligence. The study tests the hypothesis about the influence of university status on the Alliance ranking in the field of AI and the presence of specialized educational programs in AI at the university on the effectiveness of graduate employment in these programs. The stud y is based on the monitoring of universities implementing educational programs in the field of artificial intelligence (n = 191). As part of the analysis of the employment of graduates with competencies in the field of artificial intelligence, the specifics of their distribution among employment channels were determined. The authors identified key universities in terms of the effectiveness of training AI personnel, the amount of wages, and the most popular positions in employment. Based on the comparison of the employment rates of university graduates, as well as alternative sources of AI personnel training (self-education and professional retraining) with the indicators of personnel needs in the field of artificial intelligence, a conclusion was made about the provision of this need by 43.9 %. The obtained data allow us to conclude that universities have successfully implemented the Russian Government’s order to increase the volume of training of AI specialists. The value of the article lies in the presentation of unique factual materials that, for the first time, describe the employment of university graduates in the field of AI. In particular, the article details the losses of human resources in the AI field on the way from the admission of applicants to universities to the direct employment of graduates. The target audience of the article is researchers, experts, analysts, employees, and managers of universities, as well as government representatives involved in the process of developing artificial intelligence in Russia.

The rapid development in the field of artificial intelligence is bringing advances in many research fields including the renewable electricity‐driven CO 2 and CO reduction, which features as a promising approach to mitigate carbon emission and electrically synthesize value‐added chemicals. This review focuses on the recent progresses on artificial intelligence and machine learning in the field of CO (2) reduction based on the three aspects: the catalyst screening and design, reaction mechanism investigation, and the knowledge graph construction. For the catalyst development, this work discusses the acceleration of catalyst optimization and the generative artificial intelligence frameworks based on machine learning model predictions, which represent an emerging paradigm for designing efficient catalysts. For the investigation of reaction mechanisms, the combination of machine learning and in situ spectroscopies can present detailed information on the structure evolution and the electric field effects, enabling to bridge experiments and theories. For constructing the knowledge graph in the CO (2) electroreduction, the natural language processing technologies are demonstrating strong capabilities. Finally, the current challenges and potential perspectives on the interdisciplinary fields of CO (2) RR and artificial intelligence are proposed to provide inspiration for the continuous developments of this field.

Biochemical origin of the schizophrenia and of the behavioral pathologies of schizoid type

The current stage of integration of artificial intelligence (AI) technologies into Educa- tion is characterized by a gradual transition to the triad “teacher – student – artificial intelligence”. AI is gradually beginning to take on many functions previously associated with the teacher, and this brings changes to the traditional learning process, transferring it to a new, more complex level in terms of solving cognitive problems. In turn, it creates a need for teachers and lecturers to solve new didactic objectives, which requires a revision of some of the teacher’s functions and requirements for his competence in the field of AI. The purpose of the study is to develop the structure and content of a teacher’s competence in the field of AI and to determine which of the structural components of this type of competence higher education teachers are able to implement at the present stage. Based on the analysis of academic literature, the following structural components of a teacher’s competence in the field of AI were proposed: 1) motivational; 2) normative and legal; 3) information security; 4) ethical; 5) prompt engineering; 6) teaching and assessment; 7) management of the edu- cational process; 8) professional development. As part of the empirical component of the study, an online survey was conducted to determine the structural components of competence in the field of AI of higher education teachers, which they are able to implement. The respondents were 219 teach- ers of specialized disciplines from 17 universities of the Russian Federation. The results of the study showed that among the substantive components of competence in the field of AI, teachers are more proficient in such aspects as teaching and assessment (x̄ = 3,35–3,71, Мо = 4), information security (x̄ = 3,56–3,88, Мо = 4), management of the educational process (x̄ = 3,41–3,84, Мо = 4). The most difficulties for teachers at the present stage are caused by the normative and legal component (x̄ = 3,35–3,47, Мо = 3) and prompt engineering (x̄ = 2,97–3,21, Мо = 3). The structure and content of the teacher’s competence in the field of AI proposed in the paper are of a recommendatory and framework nature. Based on them, depending on the specifics of the subject area and the availability of AI technical solutions, it is possible to develop the content of the competence in the field of using AI by teachers of specific academic disciplines or specialties.