Selected Papers from i-SAIRAS 2010

The 8th International Symposium on Advanced Intelligent Systems (ISIS 2007), a biennial joint conference between Korea and Japan, focuses on artificial intelligence, intelligent systems, information technology, and their applications. ISIS 2007, held on September 5-8 at Soraksan National Park under the auspices of the Korea Fuzzy Logic and Intelligent Systems Society (KFIS), was attended by 265 researchers, engineers, and other professionals and featured 206 presentations. Of more than 20 papers preliminarily selected and reviewed by the ISIS 2007 International Program Committees, 7 were chosen for the special issue of the Journal of Advanced Computational Intelligence and Intelligent Informatics, centering on advanced intelligent systems including robotics, pattern recognition, data mining, and decision-making systems. The content and conclusions presented in these fine papers should prove both interesting and informative to specialists and generalists alike. I thank the authors and reviewers for their painstaking contributions to this special issue and Prof. Kaoru Hirota of the Tokyo Institute of Technology for inviting me to guest-edit this work.

SCIS & ISIS is a biennial international joint conference on soft computing and intelligent systems, with research ranging from fuzzy systems, neural networks, and evolutionary computation to multi-agent systems, artificial intelligence, and robotics. SCIS & ISIS 2010 consisted of the 5th International Conference on Soft Computing and Intelligent Systems (SCIS) and the 11th International Symposium on Advanced Intelligent Systems (ISIS), held at Okayama Convention Center on December 8-12, 2010. Original presentations numbered 302 and participants 322. After preliminary selection by SCIS & ISIS 2010 session chairs, we listed over 70 papers to be published in extended form in the Special Issue of the Journal of Advanced Computational Intelligence and Intelligent Informatics. After inviting these authors to submit papers for this special issue, we had two referees to review them and accepted 27 for publication in Vol.15, Nos.7 and 8 in 2011. This special issue presents 15 of these papers covering most conference topics, including fuzzy theory, learning methods, neural networks, and evolutionary computation, with a focus on reinforcement learning, multi-agent system, nonlinear estimation, and real-world applications to visual system, robotics and energy. We thank the authors and reviewers for their invaluable contributions toward making this special issue possible. We are also grateful to Editors-in-chief Prof. Toshio Fukuda of Nagoya University and Prof. Kaoru Hirota of the Tokyo Institute of Technology for inviting us to serve as Guest Editors.

The Joint Conference of the 2nd International Conference on Soft Computing and Intelligent Systems and the 5th International Symposium on Advanced Intelligent Systems (SCIS & ISIS 2004) was held at Keio University in Yokohama, Japan, on September 21-24, 2004. Over 300 papers in various fields, for example, mathematics, urban and transport planning, entertainment, intelligent control, learning, image processing, clustering, neural networks application, evolutionary computation, system modeling, fuzzy measures, and robotics were submitted to the conference. The Program Committee required reviewers in SCIS & ISIS 2004 to select excellent papers considering publication in a special issue of the Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII). Some 45 papers were selected and some of them accepted by other reviewers will be published in a two-part special issue of SCIS & ISIS 2004. In this, the first part, 13 papers have been classified into six groups — papers 1-3 under intelligent control, paper 4 under robotics, papers 5 and 6 under neural network applications, papers 7-9 under evolutionary computation applications, paper 10 under human behavior analysis, and papers 11-13 under image processing. Remaining papers currently under review will be published in the next volume. We thank the reviewers for their time and effort in making these special issues possible so quickly, and thank the JACIII editorial board, especially Profs. Hirota and Fukuda, the Editors-in-Chief and Managing Editor Kenta Uchino for their invaluable aid and advice in putting these special issues together. This issue is dedicated to the late Prof. Toshiro Terano, who passed away on February 15, 2005. He will be greatly missed.

An extended review on cyber vulnerabilities of AI technologies in space applications: Technological challenges and international governance of AI

Both human brain and computer (electronic brain) could process data and do some cognition and computation tasks. Is the cognition of human brain equal to the computation of computer? It is obviously not. In this talk, the relationships of brain cognition models and intelligence computation models are summarized into four different types, that is, human brain cognition inspired intelligence computation (BCIIC), intelligence computation without human brain cognition (ICOBC), intelligence computation assisted human brain cognition (ICABC), and the integration of human brain cognition and intelligence computation (BC&IC). There are three paradigms in traditional artificial intelligence (AI) studies, that is, symbolism AI, connectionism AI, and behaviorism AI. The physical symbol system hypothesis is used in the symbolism AI. Human brain cognition is taken as a kind of symbolic processing, and the processes of human thinking are computed by symbol in the symbolism AI [1,2]. The connectionism AI relies on the bionics to simulate human brain. In the connectionism AI, neuron is taken as the basic unite of human thinking, and the intelligence is taken as the result of interconnected neurons competition and collaboration [3,4]. In the behaviorism AI, intelligence depends on the perception and behavior, “Perception-action” model is used, and intelligence may not require knowledge, knowledge representation and knowledge reasoning [5]. The symbolism AI and connectionism AI are two different types of human brain cognition inspired intelligence computation, while the behaviorism AI is a representative case of intelligence computation without human brain cognition. Usually, AI researchers get some inspiration from human brain cognition in their studies. On the other way, intelligence computation could also assist human brain cognition studies. The bidirectional cognitive computing model (BCC) is such a case. It studies the bidirectional transformations between the intension and extension of a concept. It is used to simulate some human brain cognition tasks such as learning and recognition [6,7]. Cognitive computing is one of the core fields of artificial intelligence [8,9]. Data-driven granular cognitive computing (DGCC) is an example of the integration of human brain cognition and intelligence computation [10,11]. It takes data as a special kind of knowledge expressed in the lowest granularity level of a multiple granularity space. It integrates two contradictory mechanisms, namely, the human’s cognition mechanism of ‘‘global precedence’’ which is a cognition process of ‘‘from coarser to finer’’ and the information processing mechanism of machine learning systems which is ‘‘from finer to coarser’’, in a multiple granularity space. It is data-driven cognitive computing model. The integration of human brain cognition and intelligence computation would be an important research topic of artificial intelligence. Some scientific research issues of the integration of human brain cognition and intelligence computation are discussed based on DGCC.

Increased operational constraints on spacecraft and ground station operations have led to stricter ground software management and better performance throughput. For Mission Planning and Scheduling Systems, the ability to process numerous scheduling and planning input requests from multiple systems, applying high-level logistical rules, and providing export capabilities to all external entities, including the spacecraft itself, must be balanced against system performance. The constraint on the processing time of the schedule comes from a mission-defined need-by time, which is driven by execution or contact time. flexplan, an operational Mission Planning and Scheduling (MPS) system, has addressed the balance of performance and system capabilities. flexplan utilizes a combination of a relational database and soft-algorithm scheduling to maximize system performance. This paper demonstrates the method for the design of the internal database processing and maintenance. Performance methodologies and capabilities are presented to demonstrate software strength in two case studies: The Lunar Reconnaissance Orbiter (LRO) low lunar-orbit science mission currently operational and the Ground Network Schedule System (GNSS) multi-mission, multi-stations study of NASA’s infrastructure.

While there are numerous powerful tools to support Navy mission planning, the mission planning process still remains a hybrid planning activity across human operators and advanced tools. Advances in artificial intelligence (AI) have seen an increase in interest and use in the mission planning environment. Yet traditional approaches typically focus on optimizing the performance of the individual operator or the mission planning tool, not the joint problem solving that is needed for the human and AI team necessary in these envisioned mission planning environments. For example, while AI-based approaches can offload extensive processing by the human operator, outcomes of AI-based tools are seldom presented in ways that make it readily understand by a human or fit in with the overall process. This results in new and additional work for the operator as they must manually translate these outcomes into actionable information. There are also specific characteristics of AI-based approaches to consider, such as accounting for data limitations; many mission planning environments have data availability constraints or do not capture the right data. New methods are needed to specifically consider the benefits of AI-based approaches, ensure that AI derived insights are communicated effectively to operators, and optimally support operators in their own mission planning workflow.In this paper, we describe an overall approach for incorporating AI into the mission planning process. First, to consider how best to leverage the unique capabilities and constraints of a human operator and artificial intelligence technologies as a cooperative team throughout the mission planning process, we will frame the human operator and AI system as a single unit and take a joint cognitive system (JCS) analysis approach. This approach will identify the key information to jointly create the most effective mission plan most efficiently. Next, we describe a novel approach, neural policy programs (NPPs) to address some of the critical challenges of incorporating AI into the mission planning process. Finally, we describe our approach for extending to the StarCraft II game and creating a pre-game planning stage to prototype our approaches.KeywordsMission planningArtificial intelligenceJoint cognitive systems (JCS)Neural policy programsStarcraft II

Through the critical lens of theological ecofeminism, this article offers a nuanced analysis of the concept of the (emotional) cyborg, interrogating how the integration of technology into human (emotional) life reshapes individual identity, affective experience, and social relationality. Central to this inquiry are the dynamics of human interaction with affective computing and artificial emotional intelligence, broadly defined as technological systems capable of mimicking, interpreting, simulating, and responding to human emotions. As developments in artificial intelligence and biotechnology continue to advance, the boundary between human and machine becomes increasingly porous. Within this context, the figure of the emotional cyborg emerges as a compelling framework for examining the transformation of human emotion and connection in technologically mediated environments. This investigation is situated within broader socio-cultural concerns, particularly the contemporary quest for authenticity in a hyperconnected world, where digital interactions frequently eclipse embodied, face-to-face communication. Drawing on posthumanist and critical ecofeminist theory, this article explores the implications of the entanglement between human and artificial emotional intelligence. Among the key concerns are the potential for emotional alienation, the erosion of empathy and emotional literacy, and the commodification of affective life. In addition, the analysis engages with the implications of these shifts for pressing contemporary issues, including mental health, social isolation, and the reconfiguration of community and belonging. At its core, the presentation interrogates how emotional cyborgs intersect with gendered experiences within technologically saturated realities, and challenges and reimagines traditional theological conceptions of embodiment and relationality. The emotional cyborg, conceived as the hybridization of human emotional capacities with artificial intelligence, functions simultaneously as a reflection of and intervention in prevailing social constructions of gender, affectivity, and intersubjective connection.

Artificial intelligence emerged from human thinking that has both logical and intuitive or subjective sides. The logical side has been developed and utilized, resulting advanced von Neumann type computers and expert systems, both constituting the hard computing domain. However, it is found that hard computing can’t give the solution of very complicated problems by itself. In order to cope with this difficulty, the intuitive and subjective thinking of human mind was explored, resulting the soft computing domain (also called computational intelligence). It includes neural networks, fuzzy logic and probabilistic reasoning, the last gathering evolutionary computation (including genetic algorithms with related efforts in genetic programming and classifier systems, evolution strategies and evolutionary programming), immune networks, chaos computing and parts of learning theory. In different kind of applications, all pure artificial intelligence methods mentioned above proved to be rather complementary than competitive, so that combined methods appeared in order to gather the advantages and to cope with the disadvantages of each pure method. The scope of this chapter is to study and finaly compare some representative classes of pure and combined computational intelligence methods applied in engine knock detection.

On behalf of the Fuzzy systems and Intelligent technologies Research Society of Thailand (FIRST), Chiang Mai University, and Assumption University of Thailand, which have jointly organized the 4th International Conference on Intelligent Technology (InTech), we thank the editorial board of the Journal of Advanced Computational Intelligence and Intelligent Informatics for devoting part of this issue to selected papers from the Conference. The 4th InTech was held during December 17-19, 2003, in the lovely northern Thai city of Chiang Mai, becoming the most successful InTech ever, with over 110 papers accepted after stringent reviews for presentation at the Conference. Papers selected for publication in this special issue have been revised by their authors based on Journal guidelines. These papers detail a variety of applications of different intelligent technologies, ranging from data classification, system identification, and signal processing to full-text search and robotics. The technologies themselves are also varied, including fuzzy logic, genetic algorithms, regression, and data classification. We thank our reviewers, who have worked tirelessly in reviewing papers for the conference and selecting papers for this special issue. We also thank Professors Kaoru Hirota and Hung T. Nguyen for their ongoing support for InTech since its inception in 2000.

Nowadays artificial intelligence (AI) has been applied to many areas, including digital game with various genres. One of them is the fighting game. Implementation of AI in the fighting game aims to make players who are not constrained by the limiting factors which humans possess, such as speed of reflexes and hand gestures. One platform that can be used to make fighting game AI is FightingICE. This platform is official platform used in Computational Intelligence and Games (CIG) conference. One approach that can be used in the process of building the AI in the fighting game is opponent modeling, a method that utilize machine learning to make model based on the behavior of the opponent. There is one AI that utilize k-Nearest Neighbor (kNN) algorithm called MizunoAI. This AI has outstanding performance, but it suffers from inability to adjust its parameter automatically. To solve that problem, in this paper the algorithm used for modelling is changed to Neuroevolution of Augmenting Topologies (NEAT). NEAT is an algorithm with Artificial Neural Network (ANN) as basic architecture and Genetic Algorithm (GA) as the architecture optimizer. In the implementation, NEAT will receive six inputs of distance in the x-axis and y-axis, both players' hitpoints, and both players' stamina. Output of NEAT is the probability of all movements that can be done by the opponent. The entire output will be processed by a simulator to determine the best countermeasure based on the predictions. For testing, the AI made will be faced against four AI winners of CIG 2013, namely MizunoAI, Kaiju, T, and SejongAI. The results showed that the AI is capable to do well in modeling process. Although the performance is still behind the MizunoAI, the learning process can be seen along the match.

Introduction. Artificial intelligence (AI) today plays an important role in university education; in that sense, the objective of this research is to analyze the scientific production on AI in university education under a bibliometric review in Scopus and Web of Science (WoS). Method. A bibliometric analysis was carried out, where 895 documents published in journals indexed in the Scopus and WoS databases were analyzed. The terms ‘artificial intelligence’, ‘computational intelligence’, ‘AI’, ‘university education’ and ‘university’ were applied as terms, and the Boolean operators AND and OR were used for both databases. Results. We found 848 documents in Scopus and 48 in WoS. China is the country with the highest scientific production. Brain-Broad Research in Artificial Intelligence, Neuroscience, and Education Science are the journals with the highest number of publications in WoS, while in Scopus they are Journal of Physics Conference and Advances in Intelligent Systems and Computing. Regarding the thematic area, it was found that the highest production corresponds to computer science. In terms of affiliation, the authors belong to institutions in China, and the article with the highest number of citations is “Neuropsychological Bases of Self-Improvement of Own Physical Health of Future Teachers in the Course of University Education”. Conclusions. It is necessary to continue research on AI and its implications in university education due to its relevance in the teaching-learning processes and the interdisciplinary innovation that provides tools to reinforce knowledge.

The article discusses the concept of “artificial intelligence”, its problems and prospects. Artificial intelligence is the property of artificial intelligent systems to perform creative functions that are traditionally considered the prerogative of humans. the science and technology of creating intelligent machines, especially intelligent computer programs. The article discusses the main characteristics of artificial intelligence. Features and examples of the implementation of artificial intelligence technologies in the modern foreign educational space are shown. The history of artificial intelligence as a new scientific direction begins in the middle of the 20th century. By this time, many prerequisites for its origin had already been formed: among philosophers there had long been debates about the nature of man and the processes of cognition of the world, neurophysiologists and psychologists developed a number of theories regarding the work of the human brain and thinking, economists and mathematicians asked questions about optimal calculations and the presentation of knowledge about the world in in a formalized form; finally, the foundation of the mathematical theory of computing - the theory of algorithms - was born and the first computers were created. The formation of modern computer technologies has created a large number of cases that are related to: • human speech, the ability to learn to recognize and synthesize it; • development of technical vision mechanisms that will be able to recognize people’s faces; • the ability to teach cars to drive independently without human intervention, etc. [1]. Such systems that demonstrate and follow human behavior are called artificial intelligence. The study of artificial intelligence is an important direction in modern science. Artificial intelligence is a technology that contains a set of tools that help a computer, based on the studied data, provide answers to questions, and also draw conclusions based on this. Today, AI is essential for a wide range of industries, including healthcare, retail, manufacturing, and even government. But there are ethical challenges with AI, and as always, these options need to be out of options to ensure AI doesn't do more harm than good. Here are some of the biggest ethical challenges of AI. Keywords: Artificial intelligence, computer, robot, thinking, awareness, personality, society, humanity, problems, prospects, future.

As we all know, big data and intelligent computing have been widely used in information science, life science, computer science and intelligent control, intelligent robots, vehicle networking, space technology, marine development and other fields, especially in the future life science and medical field. This paper is based on a new digital optimal decision-making idea of intelligent signal processing and control. On the basis of extending non-digital signal processing and information prediction to signal processing and information prediction in the digital generalized information measurement space, and making full use of optimization algorithms such as digital filtering and frequency spectrum analysis, this article discusses in depth the intelligent optimal control of a class of non-stationary random processes in artificial intelligence, biomedicine, radio communication, automatic control and other scientific and technological fields. It provides a new theory, a new approach and an effective and reliable new method for the mathematical modeling and optimal control of such processes, and has broad application prospects and value.

This study addresses the convergence between technology and education, exploring the impact of paradigms such as "computational intelligence," "educational robotics," and "artificial intelligence" in educational research. The methodology was defined in three stages. In the first stage, the Web of Science database was chosen, and a search string was developed. The second stage involved the selection of studies through inclusion/exclusion criteria and the use of PRISMA. The third stage included the extraction and analysis of quantitative and qualitative data, using bibliometric software, content analysis, and tools such as R Studio, Bibliometrix, VOSViewer, and Python. An annual growth of 56.51% between 2019 and 2023, with 208 works, is revealed. "Sustainability" leads the journals with 39 articles, indicating concentration in highly productive journals. The analysis of keyword co-occurrence reveals frequents thematic areas, highlighting "artificial intelligence," "education," "technology," "machine learning," and "Big data." The lead institution is the Chinese University of Hong Kong, while China stands out with 61 papers at the country level. It emphasizes the importance of considering quality and quantity in scientific production and identifies five key topics in research summaries, suggesting areas of research focused on the integration of technology and educational innovation.