Developing Team Design Patterns for Hybrid Intelligence Systems

Summary form only given. There has been substantial progress in both intelligent systems and robotics in recent years. While in the past robots were most successful in traditional industrial environments, new generations of hybrid intelligent robotic systems are being developed which focus on higher cognitive capabilities, including reasoning learning and language communication. In this paper we give an overview of learning neural robots from a perspective of integrative hybrid intelligent systems and illustrate some new developments including also examples under development in the Centre for Hybrid Intelligent Systems.

Well-established design patterns offer the possibility of standardized construction of software systems and can be used in various ways. The systematic use of design patterns in the field of Artificial Intelligence (AI) Systems however, has received little attention so far, despite AI being a popular research area in recent years. AI systems can be used for a wide variety of applications and play an increasingly important role in business and everyday life. AI systems are becoming more complex however, the actual machine learning (ML) task comprises only a small part of the total source code of a system. In order to maintain a clear and structured architecture for such systems and to allow easy maintenance, standardized elements should be reused in the design. This paper describes possible applications of well-known design patterns in AI systems to improve traceability of the system design.

An intelligent hybrid approach has been developed to integrate various stages in total design, including formulation of product design specifications, conceptual design, detail design, and manufacture. The integration is achieved by blending multiple artificial intelligence (AI) techniques and CAD/CAE/CAM into a single environment. It has been applied into power transmission system design. In addition to knowledge-based systems and artificial neural networks, another AI technique, genetic algorithms (GAs), are involved in the approach. The GA is used to conduct optimization tasks: (1) searching the best combination of design parameters to obtain optimum design of gears, and (2) optimization of the architecture of the artificial neural networks used in the hybrid system. In this paper, after a brief overview of the intelligent hybrid system, the GA applications are described in detail.

In many domains such as management, production and government, established control approaches struggle to address increasing complexity in a timely manner, resulting in a demand for more agile methods. Hybrid intelligence and decision support systems are useful approaches to augment human decision-making through artificial intelligence (AI). Various application of AI methods to estimate production parameters or to provide forecasts are discussed in the literature or already being implemented, however, human decision-making is still required for either deciding whether to follow specific suggestions or for monitoring their respective implementation. But human behavioral research has shown that human decision-making is rather biased than fully rational, leading to unintended consequences in the collaborative work of humans and machines. Subsequently, the research stream of hybrid intelligence has gained interest recently, aiming to study the collaboration between humans and machines. We contribute to this issue by combining a systematic literature review on AI and cognitive biases combined with practical insights from discussions with experts in order to derive first guidelines addressing the human factor in the design of AI-based decision support systems for complex production environments.

Hierarchical structure, reusable and dynamic components, and predictable interactions are distinct characteristics of hybrid intelligent systems (HIS). The existing agent-oriented methodologies are deficient in HIS construction because they did not take into account the characteristics of HIS. In this paper, we propose a Methodology for constructing Agent-based HIS (MAHIS). MAHIS consists of eight models: Hybrid Strategy Identification Model, Organization Model, Task Model, Agent Model, Expertise Model, Coordination Model, Reorganization Model, and Design Model. The Reorganization Model is the key model to support dynamic platform-based HIS. It consists of category role, group roles, virtual organization role, and dynamics rules. This model describes the characteristics of HIS with virtual organization, category, and group perspectives. Some previously developed agents can be reused by means of involving them in a new virtual organization dynamically. The output of the Reorganization Model is the specification of the dynamic platform which comprises middle agents and makes all agents and agent groups hierarchical and dynamic.KeywordsMultiagent SystemAgent GroupAgent ModelTask ModelHybrid StrategyThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In this chapter, we discuss, in depth, visual information processing and a new artificial intelligent (AI) system that is based upon cognitive mechanisms. The relationship between a general model of intelligent systems and cognitive mechanisms is described, and in particular we explore visual information processing with selective attention. We also discuss a methodology for studying the new AI system and propose some important basic research issues that have emerged in the intersecting fields of cognitive science and information science. To this end, a new scheme for associative memory and a new architecture for an AI system with attractors of chaos are addressed.

We discuss how over the last 30 to 50 years, Artificial Intelligence (AI)\nsystems that focused only on data have been handicapped, and how knowledge has\nbeen critical in developing smarter, intelligent, and more effective systems.\nIn fact, the vast progress in AI can be viewed in terms of the three waves of\nAI as identified by DARPA. During the first wave, handcrafted knowledge has\nbeen at the center-piece, while during the second wave, the data-driven\napproaches supplanted knowledge. Now we see a strong role and resurgence of\nknowledge fueling major breakthroughs in the third wave of AI underpinning\nfuture intelligent systems as they attempt human-like decision making, and seek\nto become trusted assistants and companions for humans. We find a wider\navailability of knowledge created from diverse sources, using manual to\nautomated means both by repurposing as well as by extraction. Using knowledge\nwith statistical learning is becoming increasingly indispensable to help make\nAI systems more transparent and auditable. We will draw a parallel with the\nrole of knowledge and experience in human intelligence based on cognitive\nscience, and discuss emerging neuro-symbolic or hybrid AI systems in which\nknowledge is the critical enabler for combining capabilities of the\ndata-intensive statistical AI systems with those of symbolic AI systems,\nresulting in more capable AI systems that support more human-like intelligence.\n

Artificial intelligence (AI) systems are increasingly being used in consumer as well and business applications. Companies are using them, Governments are using them, public security, medical researchers, you and I – everybody seems to be somehow touched. As it becomes more advanced and ubiquitous in our lives, it is critical that AI developers and users understand the ethical implications and challenges posed by this powerful technology. This paper explores the key ethical skills and considerations that are essential for navigating the new world of AI. Artificial Intelligence (AI) presents significant ethical dimensions across various sectors, including research, business, and daily life. It is important to appreciate the major ethical dimensions of AI and how they impact AI systems. Policies and guidelines needs to be laid out for development and usage of such systems. Automated Decisions/Responsibility: No matter how intelligent a AI system might perceived to be, it is always prone to making errors. So, AI system that make automated decisions will always be dangerous. Therefore, it is important to ensure that there is always "human intervention" or "human review" for outcomes/decisions made by AI systems. Determining accountability for AI-generated actions or content remains a significant challenge, emphasizing the need for ethical governance frameworks. There has to be human accountability.Fairness/Bias: AI systems can perpetuate existing biases present in training data, leading to discriminatory outcomes.Transparency: The opaque nature of many AI algorithms complicates accountability, making it difficult to understand decision-making processes.Privacy: AI's capability to process vast datasets can infringe on individual privacy rights, necessitating stringent data protection measuresHuman Dignity: The risk of AI-generated content resembling existing works raises ethical issues regarding originality and authorship.

This article investigates how Artificial Intelligence (AI) systems reshape risk and uncertainty in dementia care by proposing a conceptual shift from technological efficacy to hybrid intelligence. Drawing on empirical research conducted in both residential and home-based care settings in Italy, and based on interviews with professional and informal caregivers, the study explores how AI technologies such as telemonitoring systems and GPS trackers not only assist but co-construct care practices. Rather than functioning as neutral tools, AI systems emerge as epistemic and moral agents that actively participate in framing vulnerability, distributing responsibility, and redefining what counts as actionable risk. The analysis reveals that far from eliminating uncertainty, AI redistributes it, often intensifying the interpretive and affective labour of caregivers. Through the lens of hybrid intelligence, the article foregrounds the relational and situated nature of human-AI collaboration and argues for a critical reconceptualisation of risk in algorithmic health environments. This reframing emphasises the socio-technical entanglements, epistemic asymmetries, and moral decisions embedded in contemporary care infrastructures.

Although the categorization of ultrasound using the Breast Imaging Reporting and Data System (BI-RADS) has become widespread worldwide, the problem of inter-observer variability remains. To maintain uniformity in diagnostic accuracy, we have developed a novel artificial intelligence (AI) system in which AI can distinguish whether a static image obtained using a breast ultrasound represents BI-RADS3 or lower, or BI-RADS4a or higher, to determine the medical management that should be performed on a patient whose breast ultrasound shows abnormalities. To establish and validate the AI system, a training dataset consisting of 4,028 images containing 5,014 lesions and a test dataset consisting of 3,166 images containing 3,656 lesions were collected and annotated. We selected a setting that maximized the area under the curve (AUC) and minimized the difference in sensitivity and specificity by adjusting the internal parameters of the AI system, achieving an AUC, sensitivity, and specificity of 0.95, 90.0%, and 88.5%, respectively. Furthermore, based on 30 images extracted from the test data, the diagnostic accuracy of 20 clinicians and the AI system was compared, and the AI system was found to be significantly superior to the clinicians (McNemar test, p < 0.001). Then, we conducted a trial to introduce the system for use in clinical practice. Physicians reviewed the images and determined whether they were BI-RADS3 or lower, or BI-RADS4a or higher. Next, the classification was performed again for the same images concerning the AI diagnosis. At this time, the initial judgment was allowed to be overturned. We checked whether there was any difference in the diagnostic accuracy, sensitivity, and specificity before and after reviewing to the AI diagnosis. Reviews by 24 physicians were evaluated: 4 Japanese Breast Cancer Society breast specialists, 5 non-specialists and physicians with experience treating more than 40 cases of breast cancer, and 15 non-specialists and physicians with no experience treating more than 40 cases of breast cancer. The average rate of accuracy before confirming the AI diagnosis increased to 73.1% after confirming the AI diagnosis (p=0.00548), compared to 69.3% on average before the AI diagnosis. Compared to practice experience, the accuracy increased from an average of 77.1% to 79.6% for the 9 physicians who were breast specialists or who had treated 40 or more cases of breast cancer. For the 15 physicians with less than 40 breast cancer cases, the average rate of accuracy increased from 64.7% to 69.2%. Furthermore, sensitivity increased significantly to an average of 99.7% after reviewing of the AI diagnosis from an average of 88.8% prior to reviewing the AI-diagnosis.(p< 0.01). Specificity increased from an average of 62.4% to 63.8% (p=0.433) after reviewing AI diagnosis. We showed that our AI system, when applied to clinical practice and used by physicians, contributes to the improvement of diagnostic accuracy. Our results indicated that our AI diagnostic system was sufficiently accurate to be used in the clinical practice. Citation Format: Erina Odani, Tetsu Hayashida, masayuki kikuchi, Aiko Nagayama, tomoko seki, maiko takahashi, Akiko Matsumoto, Takeshi Murata, Rurina Watanuki, Takamichi Yokoe, Ayako Nakashoji, Hinako Maeda, Tatsuya Onishi, Sota Asaga, Takashi Hojo, Hiromitsu Jinno, Keiichi Sotome, Akira Matsui, Akihiko Suto, Shigeru Imoto, Yuko Kitagawa. Establishment of the breast ultrasound support system using deep-learning system [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P1-05-06.

The series Studies in Computational Intelligence (SCI) publishes new developments and advances in the various areas of computational intelligence quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life science, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems and hybrid intelligent systems. Critical to both contributors and readers are the short publication time and world-wide distribution this permits a rapid and broad dissemination of research results.The field of Artificial Intelligence developed important concepts for simulating human intelligence. Its sister field, Applied Intelligence, has focused on techniques for developing intelligent systems for solving real life problems in all disciplines including science, social science, art, engineering, and finance. The objective of the International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA-AIE) is to promote and disseminate research in Applied Intelligence. It seeks quality papers on a wide range of topics in applied intelligence that are employed in developing intelligent systems for solving real life problems in all disciplines. Every year this conference brings together scientists, engineers and practitioners, who work on designing and developing applications that use intelligent techniques or work on intelligent techniques and apply them to application domains. The book is comprised of seventeen chapters providing up-to-date and state-of-the-art research on the applications of applied Intelligence techniques.

Introduction. The relevance of the study is explained by the fact that in the age of rapid development of science and technology related to the development and application of the concept of machine learning, operating with big data, artificial intelligence (AI) has achieved the ability to surpass people in each separate area. The development of hybrid intelligence faces cognitive differences, the digital gap between man and machine, and other challenges. Strengthening the interaction between cognition and perception can be the key to overcoming weaknesses. The purpose of the article is to conduct research on the development of a model of human-computer interaction, to study a new model of this process.Materials and Methods. The work uses analysis and synthesis, a comparative method. The architecture of hybrid intelligence is built on the basis of a combination of the idea of human participation in the process and the confrontation between man and machine. The method of comparative and systematic analysis of similarities and differences between human intelligence, AI, and AI systems is also used.Results. There are three main cognitive perspectives in current AI research: technological, human, and human-machine cooperation. Modern research mainly focuses on the theoretical elaboration and application of mixed intelligence and the construction of a hybrid intelligent architecture model. The confrontation between man and machine cannot be understood simply as competition between man and machine, and its ultimate goal is still to achieve a harmonious stable state of symbiosis between man and machine. It is emphasized that the interaction of man and computer involves the participation of man and machine, while the degree and status of this participation differ. “Interaction” reflects two-way information transmission and is machine-driven, while “cooperation” reflects human-dominated shared decision-making.Discussion and Conclusion. Currently, a new round of scientific and technological revolution and industrial transformation is taking place. Big data applications and innovations in theoretical algorithms are driving intelligence. Technologies such as 5G and cloud intelligence take the development of distributed artificial intelligence to the next level allowing data to generate knowledge through cloud learning. Thus, thanks to the cooperation of machine intelligence and human mind, the merger of man and machine became the ultimate goal of the intelligence development. Human mind and machine intelligence adapt to each other and realize a common evolution and optimization of intelligence. Hybrid intelligence is a highly converged combination of rationality and sensitivity, intuition and logic, memory and data storage, as well as calculations, which is the future direction of the intelligent technologies’ development.

BackgroundThe surgical difficulty of laparoscopic cholecystectomy (LC) for acute cholecystitis (AC) and the risk of bile duct injury (BDI) depend on the degree of fibrosis and scarring caused by inflammation; therefore, understanding these intraoperative findings is crucial to preventing BDI. Scarring makes it particularly difficult to perform safely and increases the BDI risk. This study aimed to develop an artificial intelligence (AI) system to indicate intraoperative findings of scarring in LC for AC.Materials and methodsAn AI system was developed to detect scarred areas using an algorithm for semantic segmentation based on deep learning. The training dataset consisted of 2025 images extracted from LC videos of 21 cases with AC. External evaluation committees (EEC) evaluated the AI system on 20 cases of untrained data from other centers. EECs evaluated the accuracy in identifying the scarred area and the usefulness of the AI system, which were assessed based on annotation and a 5-point Likert-scale questionnaire.ResultsThe average DICE coefficient for scarred areas between AI detection and EEC annotation was 0.612. The EEC’s average detection accuracy on the Likert scale was 3.98 ± 0.76. AI systems were rated as relatively useful for both clinical and educational applications.ConclusionWe developed an AI system to detect scarred areas in LC for AC. Since scarring increases the surgical difficulty, this AI system has the potential to reduce BDI.

The paper develops research in the field of building distributed artificial intelligent systems for computer modeling of collective problem solving by specialists “at a round table”, in particular, cohesive hybrid intelligent multi-agent systems. The main feature of such systems is the presence of mechanisms for automatic coordination of goals, ontologies and versions of the problem-solving protocols by its agents. These mechanisms are especially relevant, if the system is assembled from agents, developed by various independent teams, and were not initially adapted to each other to work together efficiently within a single system. This paper considers the issues of constructing one of the methods of cohesive hybrid intelligent multi-agent systems, namely, measuring the consistency of versions of the problem-solving protocol, built by the agents of the system.KeywordsCohesionHybrid intelligent multi-agent systemTeam of specialistsProtocol consistency

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