Co-evolutionary Hybrid Intelligence
and its applications

International School

CHI

International School

Library

Our publication
Useful links
  • Krinkin, K., Shichkina, Y., Ignatyev, A.: Co-evolutionary hybrid intelligence is a key concept for the world intellectualization. Kybernetes ahead-of-print (2022)
Annotation

Artificial intelligence is one of the drivers of modern technological development. The current approach to the development of intelligent systems is data-centric. It has several limitations: it is fundamentally impossible to collect data for modeling complex objects and processes; training neural networks requires huge computational and energy resources; solutions are not explainable. The article discusses an alternative approach to the development of artificial intelligence systems based on human-machine hybridization and their co-evolution.

  • Shichkina, Y., Krinkin, K., Principles of building personalized intelligent human assistants, Proceedings - 4th International Conference "Neurotechnologies and Neurointerfaces", CNN 2022, 2022, pp. 148–151
Annotation

In a context of constant evolution and proliferation of AI technology, Hybrid Intelligence is gaining popularity to refer a balanced coexistence between human and artificial intelligence. The term has been extensively used in the past two decades to define models of intelligence involving more than one technology. This paper aims to provide (i) a concise and focused overview of the adoption of Ontology in the broad context of Hybrid Intelligence regardless of its definition and (ii) a critical discussion on the possible role of Ontology to reduce the gap between human and artificial intelligence within hybrid intelligent systems. Beside the typical benefits provided by an effective use of ontologies, at a conceptual level, the conducted analysis has pointed out a significant contribution of Ontology to improve quality and accuracy, as well as a more specific role to enable extended interoperability, system engineering and explainable/transparent systems. Additionally, an application-oriented analysis has shown a significant role in present systems (70+% of the cases) and, potentially, in future systems. However, despite the relatively consistent number of papers on the topic, a proper holistic discussion on the establishment of the next generation of hybrid-intelligent environments with a balanced co-existence of human and artificial intelligence is fundamentally missed in literature. Last but not the least, there is currently a relatively low explicit focus on automatic reasoning and inference in hybrid intelligent systems.

  • Krinkin, K., Shichkina, Y. (2023). Cognitive Architecture for Co-evolutionary Hybrid Intelligence. In: Goertzel, B., Iklé, M., Potapov, A., Ponomaryov, D. (eds), Lecture Notes in Computer Science (LNAI), vol 13539. Springer, Cham. 2023, pp.293-303
Annotation

This paper questions the feasibility of a strong (general) data-centric artificial intelligence (AI). The disadvantages of this type of intelligence are discussed. As an alternative, the concept of co-evolutionary hybrid intelligence is proposed. It is based on the cognitive interoperability of man and machine. An analysis of existing approaches to the construction of cognitive architectures is given. An architecture seamlessly incorporates a human into the loop of intelligent problem solving is considered. The article is organized as follows. The first part contains a critique of data-centric intelligent systems. The reasons why it is impossible to create a strong artificial intelligence based on this type of intelligence are indicated. The second part briefly presents the concept of co-evolutionary hybrid intelligence and shows its advantages. The third part gives an overview and analysis of existing cognitive architectures. It is concluded that many do not consider humans part of the intelligent data processing process. The next part discusses the cognitive architecture for co-evolutionary hybrid intelligence, providing integration with humans. It finishes with general conclusions about the feasibility of developing intelligent systems with humans in the problem-solving loop.

  • Shichkina, Y.; Bureneva, O.; Salaurov, E.; Syrtsova, E. Assessment of a Person’s Emotional State Based on His or Her Posture Parameters. Sensors 2023, 23, 5591.
Annotation

This article is devoted to the study of the correlation between the emotional state of a person and the posture of his or her body in the sitting position. In order to carry out the study, we developed the first version of the hardware-software system based on a posturometric armchair, allowing the characteristics of the posture of a sitting person to be evaluated using strain gauges. Using this system, we revealed the correlation between sensor readings and human emotional states. We showed that certain readings of a sensor group are formed for a certain emotional state of a person. We also found that the groups of triggered sensors, their composition, their number, and their location are related to the states of a particular person, which led to the need to build personalized digital pose models for each person. The intellectual component of our hardware–software complex is based on the concept of co-evolutionary hybrid intelligence. The system can be used during medical diagnostic procedures and rehabilitation processes, as well as in controlling people whose professional activity is connected with increased psycho-emotional load and can cause cognitive disorders, fatigue, and professional burnout and can lead to the development of diseases.

  • Madhan, K., Shiva Prakash, S., Krinkin, K.: Ensemble method for user activity classification in ambient assisted living. In: 2022 International Conference on Innovative Trends in Information Technology (ICITIIT). pp. 1–7. IEEE (2022)
Annotation

Artificial Intelligence(AI) has become a global plat-form that allows objects in IoT to Interact and perform computations. The wide range of application areas of IoT are Smart Cities, Smart grids, Smart Supply chain and Ambient Assisted Living(AAL). These applications have challenges like tolerance to uncertainty,adaptiveness to the changing environment and improved trust among users. Thus, machine learning algorithms improve the performance of smart objects in various environment. The AAL environment deploys heterogeneous devices and sensors to capture various activities carried out through the daily by the individuals who resides in the smart home. In this work, an ensemble method using k-Nearest Neighbor(KNN), Decision Tree(DT) and Logistic Regression(LR)is proposed by investigating the performance of existing conventional supervised machine learning algorithms and selecting best model by considering the sensors features and improves the performance metrics. The work is evaluated using the benchmark ARAS (Activity Recognition with Ambient Sensing) dataset. The results are analysed using different parameters. The comparative analysis show that the proposed ensemble method gives accuracy of 76.28%.

  • Mohan, G., Raje, U., Shiva Prakash, S.P., Krinkin, K.: Artificial neural network alert classifier for construction equipment telematics (cet). In: Intelligent System Design: Proceedings of INDIA 2022. pp. 147–155. Springer Nature Singapore (2022)
Annotation

The Internet of Things (IoT) is connection between the Internet of Things via cloud platform or centralized platform. It can be useful to many applications that deal with varieties of services like sharing the information from one device to another. Similarly, on these concepts, a concept called telematics, which deals with the long-distance transmission of computerized information. It gives the navigations, routing, or network-related information for many applications in service providers like transportations, logistics, travelling, and many more. It has many challenges, namely prediction of failure in the system, diagnostics analysis, etc. Therefore, there is a need in predictive analysis of CET to analyse the failure in the system. Hence, the proposed work using artificial neural network to alert the system. The experiment is conducted using ANN on CET data set, with obtained the metric of accuracy 100%. Also analysed the various machine learning (ML) algorithm, namely DT, KNN, and Naive Bayes classifiers obtained in the metric of accuracy of 93.72%, 93.19%, and 62.57%, respectively.

Library

Our publication
Useful links
  • Krinkin, K., Shichkina, Y., Ignatyev, A.: Co-evolutionary hybrid intelligence is a key concept for the world intellectualization. Kybernetes ahead-of-print (2022)
Annotation

Artificial intelligence is one of the drivers of modern technological development. The current approach to the development of intelligent systems is data-centric. It has several limitations: it is fundamentally impossible to collect data for modeling complex objects and processes; training neural networks requires huge computational and energy resources; solutions are not explainable. The article discusses an alternative approach to the development of artificial intelligence systems based on human-machine hybridization and their co-evolution.

  • Shichkina, Y., Krinkin, K., Principles of building personalized intelligent human assistants, Proceedings - 4th International Conference "Neurotechnologies and Neurointerfaces", CNN 2022, 2022, pp. 148–151
Annotation

In a context of constant evolution and proliferation of AI technology, Hybrid Intelligence is gaining popularity to refer a balanced coexistence between human and artificial intelligence. The term has been extensively used in the past two decades to define models of intelligence involving more than one technology. This paper aims to provide (i) a concise and focused overview of the adoption of Ontology in the broad context of Hybrid Intelligence regardless of its definition and (ii) a critical discussion on the possible role of Ontology to reduce the gap between human and artificial intelligence within hybrid intelligent systems. Beside the typical benefits provided by an effective use of ontologies, at a conceptual level, the conducted analysis has pointed out a significant contribution of Ontology to improve quality and accuracy, as well as a more specific role to enable extended interoperability, system engineering and explainable/transparent systems. Additionally, an application-oriented analysis has shown a significant role in present systems (70+% of the cases) and, potentially, in future systems. However, despite the relatively consistent number of papers on the topic, a proper holistic discussion on the establishment of the next generation of hybrid-intelligent environments with a balanced co-existence of human and artificial intelligence is fundamentally missed in literature. Last but not the least, there is currently a relatively low explicit focus on automatic reasoning and inference in hybrid intelligent systems.

  • Krinkin, K., Shichkina, Y. (2023). Cognitive Architecture for Co-evolutionary Hybrid Intelligence. In: Goertzel, B., Iklé, M., Potapov, A., Ponomaryov, D. (eds), Lecture Notes in Computer Science (LNAI), vol 13539. Springer, Cham. 2023, pp.293-303
Annotation

This paper questions the feasibility of a strong (general) data-centric artificial intelligence (AI). The disadvantages of this type of intelligence are discussed. As an alternative, the concept of co-evolutionary hybrid intelligence is proposed. It is based on the cognitive interoperability of man and machine. An analysis of existing approaches to the construction of cognitive architectures is given. An architecture seamlessly incorporates a human into the loop of intelligent problem solving is considered. The article is organized as follows. The first part contains a critique of data-centric intelligent systems. The reasons why it is impossible to create a strong artificial intelligence based on this type of intelligence are indicated. The second part briefly presents the concept of co-evolutionary hybrid intelligence and shows its advantages. The third part gives an overview and analysis of existing cognitive architectures. It is concluded that many do not consider humans part of the intelligent data processing process. The next part discusses the cognitive architecture for co-evolutionary hybrid intelligence, providing integration with humans. It finishes with general conclusions about the feasibility of developing intelligent systems with humans in the problem-solving loop.

  • Shichkina, Y.; Bureneva, O.; Salaurov, E.; Syrtsova, E. Assessment of a Person’s Emotional State Based on His or Her Posture Parameters. Sensors 2023, 23, 5591.
Annotation

This article is devoted to the study of the correlation between the emotional state of a person and the posture of his or her body in the sitting position. In order to carry out the study, we developed the first version of the hardware-software system based on a posturometric armchair, allowing the characteristics of the posture of a sitting person to be evaluated using strain gauges. Using this system, we revealed the correlation between sensor readings and human emotional states. We showed that certain readings of a sensor group are formed for a certain emotional state of a person. We also found that the groups of triggered sensors, their composition, their number, and their location are related to the states of a particular person, which led to the need to build personalized digital pose models for each person. The intellectual component of our hardware–software complex is based on the concept of co-evolutionary hybrid intelligence. The system can be used during medical diagnostic procedures and rehabilitation processes, as well as in controlling people whose professional activity is connected with increased psycho-emotional load and can cause cognitive disorders, fatigue, and professional burnout and can lead to the development of diseases.

  • Madhan, K., Shiva Prakash, S., Krinkin, K.: Ensemble method for user activity classification in ambient assisted living. In: 2022 International Conference on Innovative Trends in Information Technology (ICITIIT). pp. 1–7. IEEE (2022)
Annotation

Artificial Intelligence(AI) has become a global plat-form that allows objects in IoT to Interact and perform computations. The wide range of application areas of IoT are Smart Cities, Smart grids, Smart Supply chain and Ambient Assisted Living(AAL). These applications have challenges like tolerance to uncertainty,adaptiveness to the changing environment and improved trust among users. Thus, machine learning algorithms improve the performance of smart objects in various environment. The AAL environment deploys heterogeneous devices and sensors to capture various activities carried out through the daily by the individuals who resides in the smart home. In this work, an ensemble method using k-Nearest Neighbor(KNN), Decision Tree(DT) and Logistic Regression(LR)is proposed by investigating the performance of existing conventional supervised machine learning algorithms and selecting best model by considering the sensors features and improves the performance metrics. The work is evaluated using the benchmark ARAS (Activity Recognition with Ambient Sensing) dataset. The results are analysed using different parameters. The comparative analysis show that the proposed ensemble method gives accuracy of 76.28%.

  • Mohan, G., Raje, U., Shiva Prakash, S.P., Krinkin, K.: Artificial neural network alert classifier for construction equipment telematics (cet). In: Intelligent System Design: Proceedings of INDIA 2022. pp. 147–155. Springer Nature Singapore (2022)
Annotation

The Internet of Things (IoT) is connection between the Internet of Things via cloud platform or centralized platform. It can be useful to many applications that deal with varieties of services like sharing the information from one device to another. Similarly, on these concepts, a concept called telematics, which deals with the long-distance transmission of computerized information. It gives the navigations, routing, or network-related information for many applications in service providers like transportations, logistics, travelling, and many more. It has many challenges, namely prediction of failure in the system, diagnostics analysis, etc. Therefore, there is a need in predictive analysis of CET to analyse the failure in the system. Hence, the proposed work using artificial neural network to alert the system. The experiment is conducted using ANN on CET data set, with obtained the metric of accuracy 100%. Also analysed the various machine learning (ML) algorithm, namely DT, KNN, and Naive Bayes classifiers obtained in the metric of accuracy of 93.72%, 93.19%, and 62.57%, respectively.