Hybrid intelligent systems and cognitive robotics

With artificial intelligence (AI) systems entering our working and leisure environments with increasing adaptation and learning capabilities, new opportunities arise for developing hybrid (human-AI) intelligence (HI) systems, comprising new ways of collaboration. However, there is not yet a structured way of specifying design solutions of collaboration for hybrid intelligence (HI) systems and there is a lack of best practices shared across application domains. We address this gap by investigating the generalization of specific design solutions into design patterns that can be shared and applied in different contexts. We present a human-centered bottom-up approach for the specification of design solutions and their abstraction into team design patterns. We apply the proposed approach for 4 concrete HI use cases and show the successful extraction of team design patterns that are generalizable, providing re-usable design components across various domains. This work advances previous research on team design patterns and designing applications of HI systems.

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.

A hybrid intelligent system for medical data classification

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

Solar wind magnetosphere coupling: Predicted and modeled with intelligent hybrid systems

BackgroundExpert knowledge in journal articles is an important source of data for reconstructing biological pathways and creating new hypotheses. An important need for medical research is to integrate this data with high throughput sources to build useful models that span several scales. Researchers traditionally use mental models of pathways to integrate information and development new hypotheses. Unfortunately, the amount of information is often overwhelming and these are inadequate for predicting the dynamic response of complex pathways. Hierarchical computational models that allow exploration of semi-quantitative dynamics are useful systems biology tools for theoreticians, experimentalists and clinicians and may provide a means for cross-communication.ResultsA novel approach for biological pathway modeling based on hybrid intelligent systems or soft computing technologies is presented here. Intelligent hybrid systems, which refers to several related computing methods such as fuzzy logic, neural nets, genetic algorithms, and statistical analysis, has become ubiquitous in engineering applications for complex control system modeling and design. Biological pathways may be considered to be complex control systems, which medicine tries to manipulate to achieve desired results. Thus, hybrid intelligent systems may provide a useful tool for modeling biological system dynamics and computational exploration of new drug targets. A new modeling approach based on these methods is presented in the context of hedgehog regulation of the cell cycle in granule cells. Code and input files can be found at the Bionet website: www.chip.ord/~wbosl/Software/Bionet.ConclusionThis paper presents the algorithmic methods needed for modeling complicated biochemical dynamics using rule-based models to represent expert knowledge in the context of cell cycle regulation and tumor growth. A notable feature of this modeling approach is that it allows biologists to build complex models from their knowledge base without the need to translate that knowledge into mathematical form. Dynamics on several levels, from molecular pathways to tissue growth, are seamlessly integrated. A number of common network motifs are examined and used to build a model of hedgehog regulation of the cell cycle in cerebellar neurons, which is believed to play a key role in the etiology of medulloblastoma, a devastating childhood brain cancer.

A hybrid stock trading system for intelligent technical analysis-based equivolume charting

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.

There are many types of intelligent systems used in modeling the information processing at the human brain level. The information processing is divided into two major parts: first part represents the information processing at the microstructure level (represented by sensory processes) and second part represents the information processing at the macrostructure level (represented by reasoning, memory and problem solving). In this context, the neural networks are used for model the first part of learning process - the primary stage of this and expert systems are used for model the second part of learning process - the final stage of this. So, the information processing is model by a complex system which contains many intelligent control systems: neural network, expert system and hybrid intelligent systems

Rapid development in computer and sensor technology not only used for highly specialised applications but widespread and pervasive across a wide range of business and industry has facilitated easy capture and storage of immense amounts of data. Examples of such data collection include medical history data in health care, financial data in banking, point of sale data in retail, plant monitoring data based on instant availability of various sensor readings in various industries, or airborne hyperspectral imaging data in natural resources identification to mention only a few. However, with an increasing computer power available at affordable prices and the availability of vast amount of data there is an increasing need for robust methods and systems, which can take advantage of all available information. In essence there is a need for intelligent and smart adaptive methods but do they really exist? Are there any existing intelligent techniques which are more suitable for certain type of problems than others? How do we select those methods and can we be sure that the method of choice is the best for solving our problem? Do we need a combination of methods and if so then how to best combine them for different purposes? Are there any generic frameworks and requirements which would be highly desirable for solving data intensive and unstationary problems? All these questions and many others have been the focus of research vigorously pursued in many disciplines and some of them will be discussed in the talk and have been addressed in greater detail in our recently compiled book with the same title: ”Do Smart Adaptive Systems Exist?”. One of the more promising approaches to constructing smart adaptive systems is based on intelligent technologies including artificial neural networks, fuzzy systems, methods from machine learning, parts of learning theory and evolutionary computing which have been especially successful in applications where input-output data can be collected but the underlying physical model is unknown. The incorporation of intelligent technologies has been used in the conception and design of complex systems in which analytical and expert systems techniques are used in combination. Viewed from a much broader perspective, the above mentioned intelligent technologies are constituents of a very active research area known under the names of soft computing, computational intelligence or hybrid intelligent systems. However, hybrid soft computing frameworks are relatively young, even comparing to the individual constituent technologies, and a lot of research is required to understand their strengths and weaknesses. Nevertheless hybridization and combination of intelligent technologies within a flexible open framework seem to be the most promising direction in achieving the truly smart and adaptive systems today. Despite all the challenges it is unquestionable that smart adaptive intelligent systems and intelligent technology have started to have a huge impact on our everyday life and many applications can already be found in various commercially available products as illustrated in the recent report compiled by one of the world’s leading think tank advanced technology organisations and very suggestively titled: ”Get smart: How intelligent technology will enhance our world”.

Artificial Immune Systems (AIS) are sometimes still considered with an attitude of reserve by most practitioners in Computational Intelligence (CI), much more, some of them even considering this emergent computing paradigm to be in an infancy stage. Recently, however, a lot of basic models and algorithms based on components of the artificial immune-based systems have been developed and are used in a variety of applications. Further on, the focusing of Computational Intelligence on the emerging field of the AIS led to very effective hybrid intelligent systems of AIS with other CI paradigms. This tutorial will present why AIS are of interest, starting from the real- world of applications asking for a radical change of the information systems framework. The component-based framework is replaced with an agent- based one, where the system complexity requires that any agent be clearly featured by its autonomy. The AIS methods build adaptive large-scale multi- agent systems that are open to the environment, systems that are not at all fixed just after the design phase, but are real-time adaptive to unpredictable situations and malicious defects. The AIS perform the defence of a complex system against malicious defects achieving its survival strategy by extension of the concept of organization of multicellular organisms to the information systems. The main behavioural features of the AIS - as self-maintenance, distributed and adaptive computational systems - are defined and described in relation to the Immune System as an information system. A comparison of the AIS methodology with other Intelligent Technologies is another point of the tutorial. The overview of some actual AIS applications is made using a practical engineering design strategy that views AIS as very effective software with agent-based architecture.

The area of cognitive or intelligent robotics is moving from the single robot control and behavior problem to that of controlling multiple robots operating together and even collaborating in dynamic and unstructured environments. This paper introduces the topic and provides a general overview of the current state of the field of modular and multi robotics taking both of these subareas as different representations of the same problem: how to coordinate multiple elements in order to perform useful tasks. The review shows where Hybrid Intelligent Systems could provide key contributions to the advancement of the field.KeywordsIntelligent roboticsmulti-robot systemsmodular robotics

Abstract: Augmented Human Intelligence (AHI) represents a paradigm shift in human-AI collaboration, leveraging Generative AI, Quantum Computing, and Extended Reality (XR) to enhance cognitive capabilities, decision-making, and immersive interactions. Generative AI enables real-time knowledge augmentation, automated creativity, and adaptive learning, while Quantum Computing accelerates AI optimization, pattern recognition, and complex problem-solving. XR technologies provide intuitive, immersive environments for AI-driven collaboration, bridging the gap between digital and physical experiences. The convergence of these technologies fosters hybrid intelligence, where AI amplifies human potential rather than replacing it. This research explores AI-augmented cognition, quantum-enhanced simulations, and AI-driven spatial computing, addressing ethical, security, and societal implications of human-machine synergy. By integrating decentralized AI governance, privacy-preserving AI techniques, and brain-computer interfaces, this study outlines a scalable framework for next-generation augmented intelligence applications in healthcare, enterprise intelligence, scientific discovery, and immersive learning. The future of AHI lies in hybrid intelligence systems that co-evolve with human cognition, ensuring responsible and transparent AI augmentation to unlock new frontiers in human potential. Keywords: Augmented Human Intelligence, Generative AI, Quantum Computing, Extended Reality, XR, AI-driven Cognition, Hybrid Intelligence, Brain-Computer Interfaces, AI Ethics, AI-enhanced Learning, Spatial Computing, Quantum AI, Immersive AI, Human-AI Collaboration, Ethical AI Frameworks.

Automatic classification of the electrocardiogram (ECG) signals is an important subject for clinical diagnosis of heart disease. This study investigates the design of a high-efficient system to classify five types of ECG beat namely normal beats and four manifestations of heart arrhythmia, in twofold. First, we propose a system that includes two main modules: a feature extraction module and a classification module. Feature extraction module extracts a suitable combination of the ECG's morphological characteristics and timing interval features. Discrete wavelet transform is used to extract the morphological features. In the classification module, a multi-class support vector machine (SVM)-based classifier is employed. The parameters of this system are determined based on a trial and error method and its performance is evaluated for the MIT-BIH arrhythmia database. Extensive experiments on the parameters of this system such as classifier kernels and various types of features are conducted. These experiments show that in SVM training, the kernels, kernel parameters, and feature selection have very important roles for SVM classification accuracy. Therefore, most appropriates of these parameters should be used for SVM training. Then at the second fold, a novel hybrid intelligent system (HIS) is proposed that consists of three main modules. In the HIS, further to the two mentioned modules, an optimization module is added. In this module, a genetic algorithm is used for optimization of the relevant parameters of system. These parameters are: wavelet filter type for feature extraction, wavelet decomposition level, and classifier's parameters. Experimental results show that optimization improves the recognition system, efficiently, and HIS is more superior to the system, which as constant parameters.

The paper studies the basic problems of Artificial Intelligence, such as integration of difference attributes of human intellect. For this purpose we have been created synergetic systems that are hybrid intelligent systems (HYIS). The paper shows the world of decision support problems and the world of modelling approaches evolution. The term ‘heterogeneous problem’ for decision support systems is discussed. Two models of interaction between the problems world and the methods world also the results of HYIS creating are discussed. The formalism of HYIS is introduced.