Artificial Cognitive Systems Research Articles

Trustworthy AI for semiconductor manufacturing

Trustworthy AI for semiconductor manufacturing Marcel van Gerven, a Professor of Artificial Cognitive Systems at Radboud University, discusses how his team’s research on AI models, algorithms, and demonstrators could serve as a blueprint for the semiconductor manufacturing industry. Artificial Intelligence (AI) is a key enabling technology that, when used responsibly, can bring about significant positive societal impact. One of the initiatives to foster this positive impact is the Dutch ROBUST ‘Trustworthy AI-based Systems for Sustainable Growth’ program. This ten-year Long Term Program (LTP) is a human- centered research program in AI that brings together knowledge institutes, industry, governmental organizations, and societal partners to develop AI-based methods and tools designed to create social impact and promote sustainable growth (Figure 1).

Journalism in a new informative environment structured by Artificial Cognitive Systems

Journalism has evolved over the centuries, adapting to capture the audience's attention. With the emergence of intelligent systems such as chatbots and software that transform data into news narratives, giving rise to Artificial Media, computational systems with cognitive bias create content in real-time through interactive engagement with the audience. This new information ecosystem blurs the boundaries between human-generated content and that produced by machines. The introduction of Artificial Cognitive Systems establishes a symbiotic partnership between biological and artificial agents, transforming the way information is consumed. This Human Information Interaction replaces the previous master-slave relationship from the Industrial Revolution. The article suggests the hypothesis of the consolidation of Artificial Media, in which the machine can process events, contexts, and actions, initially dealing with immediate situations and increasingly acquiring predictive or prospective capabilities, thus reshaping the production, distribution, and consumption of news.

Framing the predictive mind: why we should think again about Dreyfus

AbstractIn this paper I return to Hubert Dreyfus’ old but influential critique of artificial intelligence, redirecting it towards contemporary predictive processing models of the mind (PP). I focus on Dreyfus’ arguments about the “frame problem” for artificial cognitive systems, and his contrasting account of embodied human skills and expertise. The frame problem presents as a prima facie problem for practical work in AI and robotics, but also for computational views of the mind in general, including for PP. Indeed, some of the issues it presents seem more acute for PP, insofar as it seeks to unify all cognition and intelligence, and aims to do so without admitting any cognitive processes or mechanisms outside of the scope of the theory. I contend, however, that there is an unresolved problem for PP concerning whether it can both explain all cognition and intelligent behavior as minimizing prediction error with just the core formal elements of the PP toolbox, and also adequately comprehend (or explain away) some of the apparent cognitive differences between biological and prediction-based artificial intelligence, notably in regard to establishing relevance and flexible context-switching, precisely the features of interest to Dreyfus’ work on embodied indexicality, habits/skills, and abductive inference. I address several influential philosophical versions of PP, including the work of Jakob Hohwy and Andy Clark, as well as more enactive-oriented interpretations of active inference coming from a broadly Fristonian perspective.

Prediction of Degraded Infrastructure Conditions for Railway Operation.

In the railway sector, rolling stock and infrastructure must be maintained in perfect condition to ensure reliable and safe operation for passengers. Climate change is affecting the urban and regional infrastructure through sea level rise, water accumulations, river flooding, and other increased-frequency extreme natural situations (heavy rains or snows) which pose a challenge to maintenance. In this paper, the use of artificial intelligence based on predictive maintenance implementation is proposed for the early detection of degraded conditions of a bridge due to extreme climatic conditions. For this prediction, continuous monitoring is proposed, with the aim of establishing alarm thresholds to detect dangerous situations, so restrictions could be determined to mitigate the risk. However, one of the main challenges for railway infrastructure managers nowadays is the high cost of monitoring large infrastructures. In this work, a methodology for monitoring railway infrastructures to define the optimal number of transductors that are economically viable and the thresholds according to which infrastructure managers can make decisions concerning traffic safety is proposed. The methodology consists of three phases that use the application of machine learning (Random Forest) and artificial cognitive systems (LSTM recurrent neural networks).

Skill Development through Artificial Cognitive Systems and Social Robotics Applied at Tech-Education

The focus of this research is to demonstrate how a platform composed of systems of artificial cognitive agents and social robotics can interact, teach and learn with students and teachers, through a pedagogical practice and methodological integration of the psychological concepts of the Theory of Multiple Intelligences, the educational foundations of the Dialectic methodology and the relationship explored in the Man-Machine Symbiosis. The objective of this article is to present the cognitive methodological concept of the project developed, grounding it from the theoretical principles, to the selection criteria for the models presented in the current discussions, taking as lines of thought that seek to define the strategy for creating personalized learning storytelling, the techniques for building social robotics in education and the interactivity between visual feedback in the challenging context of technological education.

Human beings in a civilization of cognitive technologies

The problematics of civilization development and the place of human being in it is a significant area of research, which is additionally actualized nowadays in the conditions of the outlined transition to a new stage - the civilization of cognitive technologies. According to the assessment proposed in the article, three stages of civilization development should be distinguished: agrarian, machine, and the civilization of cognitive technologies, which is currently being formed. It is characterized by the main need in the form of artificial cognitive systems for intellectual control of machines for the production of material goods, and the result of satisfying this need is the replacement of human beings by artificial cognitive systems in the intellectual control of machines. The following several questions are key in the context of the mentioned problems: "What are the functions of human beings in the existence and development of civilization?", "Do these functions change?", "Are there inalienable ones among them?", "What can become the basis for the further development of civilization?". The functions of human beings within civilization are the generation of needs and work to satisfy them. Generation of needs is an inalienable function of human being in civilization, which determines the determining role of the evolution of needs in the development of civilization. The main role in the evolution of needs belongs to social mechanisms of compulsion (motivation) to consumption, which influence not only the satisfaction of social needs, but also stimulate excessive consumption of material goods and intellectual activity. Further development of civilization will preserve the position of human being as an initiator of this development only in case of successful implementation of social coercion mechanisms. The condition for this is social integration: preservation and expansion of ties between human beings, counteraction to the fragmentation of society.

Leveraging natural cognitive systems in conjunction with ResNet50-BiGRU model and attention mechanism for enhanced medical image analysis and sports injury prediction.

In this study, we explore the potential benefits of integrating natural cognitive systems (medical professionals' expertise) and artificial cognitive systems (deep learning models) in the realms of medical image analysis and sports injury prediction. We focus on analyzing medical images of athletes to gain valuable insights into their health status. To synergize the strengths of both natural and artificial cognitive systems, we employ the ResNet50-BiGRU model and introduce an attention mechanism. Our goal is to enhance the performance of medical image feature extraction and motion injury prediction. This integrated approach aims to achieve precise identification of anomalies in medical images, particularly related to muscle or bone damage. We evaluate the effectiveness of our method on four medical image datasets, specifically pertaining to skeletal and muscle injuries. We use performance indicators such as Peak Signal-to-Noise Ratio and Structural Similarity Index, confirming the robustness of our approach in sports injury analysis. Our research contributes significantly by providing an effective deep learning-driven method that harnesses both natural and artificial cognitive systems. By combining human expertise with advanced machine learning techniques, we offer a comprehensive understanding of athletes' health status. This approach holds potential implications for enhancing sports injury prevention, improving diagnostic accuracy, and tailoring personalized treatment plans for athletes, ultimately promoting better overall health and performance outcomes. Despite advancements in medical image analysis and sports injury prediction, existing systems often struggle to identify subtle anomalies and provide precise injury risk assessments, underscoring the necessity of a more integrated and comprehensive approach.

A Mathematical Framework for Enriching Human–Machine Interactions

This paper presents a conceptual mathematical framework for developing rich human–machine interactions in order to improve decision-making in a social organisation, S. The idea is to model how S can create a “multi-level artificial cognitive system”, called a data analyser (DA), to collaborate with humans in collecting and learning how to analyse data, to anticipate situations, and to develop new responses, thus improving decision-making. In this model, the DA is “processed” to not only gather data and extend existing knowledge, but also to learn how to act autonomously with its own specific procedures or even to create new ones. An application is given in cases where such rich human–machine interactions are expected to allow the DA+S partnership to acquire deep anticipation capabilities for possible future changes, e.g., to prevent risks or seize opportunities. The way the social organization S operates over time, including the construction of DA, is described using the conceptual framework comprising “memory evolutive systems” (MES), a mathematical theoretical approach introduced by Ehresmann and Vanbremeersch for evolutionary multi-scale, multi-agent and multi-temporality systems. This leads to the definition of a “data analyser–MES”.

A cognitive architecture theory of anticipatory thinking

AbstractWe theorize that anticipatory thinking (AT) uses the same computational infrastructure as general cognition as described in the Common Model of Cognition. We extend the Common Model with results from research on event cognition. Using these building blocks, we present a five‐step process model of AT as realized in cognitive architecture components. We then revisit simplifying assumptions underlying our model and expand our theory in response. Finally, we make predictions that are entailed by our account of AT, focusing on how computational limits in both natural and artificial cognitive systems can impact support for AT.

Оптимальний комплекс операцій технічного обслуговування і ремонту для підвищення надійності вузлів, систем та агрегатів мобільних машин

The work examines the improvement of the forms and methods of organizing maintenance and repair systems of mobile machines in order to increase their reliability by optimizing diagnostic operations and monitoring the technical condition of their units, systems and units. The proposed centralized system of maintenance and repair of machines includes the principles of production and is characterized by the wide use of technological complexes of operations of the system of maintenance and repair of nodes, systems, aggregates and mobile machines as a whole. A comparison was made of the methodology for implementing the proposed maintenance and repair system and the world-famous RCM (Reliability-centered Maintenance) methodology, focused on a certain level of reliability. Schematics of the technological process of implementing a complex of maintenance and repair operations based on one-stage and multi-stage control of the technical condition of nodes, systems, aggregates and the mobile machine as a whole are presented. A decision-making algorithm has been developed for the processes of a complex of maintenance and repair operations. Clarified technological and information support of complexes of maintenance and repair operations of nodes, systems, units of mobile machines. The role of the operator-diagnostic in relation to the probability of receiving errors of the 1st and 2nd type of defects of parts, as well as the use of computerized workplaces, is revealed. It is shown how these factors affect the level of reliability of nodes, systems, aggregates and mobile machines as a whole. It was determined that in order to reduce the influence of the human factor on the results of the diagnosis of the presence or absence of defects and the distribution of nodes, systems, aggregates according to the technological routes of optimal complexes of maintenance and repair operations, consideration of the repair and maintenance system as an artificial cognitive system can help.

Artificial Cognitive Systems Applied in Executive Function Stimulation and Rehabilitation Programs: A Systematic Review.

This article presents a systematic review of studies on cognitive training programs based on artificial cognitive systems and digital technologies and their effect on executive functions. The aim has been to identify which populations have been studied, the characteristics of the implemented programs, the types of implemented cognitive systems and digital technologies, the evaluated executive functions, and the key findings of these studies. The review has been carried out following the PRISMA protocol; five databases have been selected from which 1889 records were extracted. The articles were filtered following established criteria, to give a final selection of 264 articles that have been used for the purposes of this study in the analysis phase. The findings showed that the most studied populations were school-age children and the elderly. The most studied executive functions were working memory and attentional processes, followed by inhibitory control and processing speed. Many programs were commercial, customizable, gamified, and based on classic tasks. Some more recent initiatives have begun to incorporate user-machine interfaces, robotics, and virtual reality, although studies on their effects remain scarce. The studies recognize multiple benefits of computerized neuropsychological stimulation and rehabilitation programs for executive functions in different age groups, but there is a lack of studies in specific population sectors and with more rigorous research designs.Supplementary InformationThe online version contains supplementary material available at 10.1007/s13369-022-07292-5.

Parallel cognition: hybrid intelligence for human-machine interaction and management

As an interdisciplinary research approach, traditional cognitive science adopts mainly the experiment, induction, modeling, and validation paradigm. Such models are sometimes not applicable in cyber-physical-social-systems (CPSSs), where the large number of human users involves severe heterogeneity and dynamics. To reduce the decision-making conflicts between people and machines in human-centered systems, we propose a new research paradigm called parallel cognition that uses the system of intelligent techniques to investigate cognitive activities and functionals in three stages: descriptive cognition based on artificial cognitive systems (ACSs), predictive cognition with computational deliberation experiments, and prescriptive cognition via parallel behavioral prescription. To make iteration of these stages constantly on-line, a hybrid learning method based on both a psychological model and user behavioral data is further proposed to adaptively learn an individual’s cognitive knowledge. Preliminary experiments on two representative scenarios, urban travel behavioral prescription and cognitive visual reasoning, indicate that our parallel cognition learning is effective and feasible for human behavioral prescription, and can thus facilitate human-machine cooperation in both complex engineering and social systems.

TripleBrain: A Compact Neuromorphic Hardware Core With Fast On-Chip Self-Organizing and Reinforcement Spike-Timing Dependent Plasticity.

Human brain cortex acts as a rich inspiration source for constructing efficient artificial cognitive systems. In this paper, we investigate to incorporate multiple brain-inspired computing paradigms for compact, fast and high-accuracy neuromorphic hardware implementation. We propose the TripleBrain hardware core that tightly combines three common brain-inspired factors: the spike-based processing and plasticity, the self-organizing map (SOM) mechanism and the reinforcement learning scheme, to improve object recognition accuracy and processing throughput, while keeping low resource costs. The proposed hardware core is fully event-driven to mitigate unnecessary operations, and enables various on-chip learning rules (including the proposed SOM-STDP & R-STDP rule and the R-SOM-STDP rule regarded as the two variants of our TripleBrain learning rule) with different accuracy-latency tradeoffs to satisfy user requirements. An FPGA prototype of the neuromorphic core was implemented and elaborately tested. It realized high-speed learning (1349 frame/s) and inference (2698 frame/s), and obtained comparably high recognition accuracies of 95.10%, 80.89%, 100%, 94.94%, 82.32%, 100% and 97.93% on the MNIST, ETH-80, ORL-10, Yale-10, N-MNIST, Poker-DVS and Posture-DVS datasets, respectively, while only consuming 4146 (7.59%) slices, 32 (3.56%) DSPs and 131 (24.04%) Block RAMs on a Xilinx Zynq-7045 FPGA chip. Our neuromorphic core is very attractive for real-time resource-limited edge intelligent systems.

Inverted spike-rate-dependent plasticity due to charge traps in a metal-oxide memristive device

We develop a model of Au/Ta/ZrO2(Y)/Ta2O5/TiN/Ti memristive devices and demonstrate, both experimentally and numerically, an inverted spike-rate-dependent plasticity effect. The effect consists of the reduction of the learning rate with an increase in the frequency of spikes generated by the phase-locked loop neuron. The memristor model uses two internal state variables representing the number of complete filaments and the concentration of the charged traps. While the former state variable defines the device resistance and is associated with the distribution of oxygen vacancies, the latter affects the internal electric field and modulates the migration of vacancies. Several neural circuit configurations that include pairs and populations of memristively coupled neurons are analyzed numerically. The results of this study may contribute to the development of large-scale self-organized artificial cognitive systems based on neural synchrony.

Theory of Knowledge Based on the Idea of the Discursive Space

This paper discusses the theory of knowledge based on the idea of dynamical space. The goal of this effort is to comprehend the knowledge that remains beyond the human domain, e.g., of the artificial cognitive systems. This theory occurs in two versions, weak and strong. The weak version is limited to knowledge in which retention and articulation are performed through the discourse. The strong version is general and is not limited in any way. In the weak version, knowledge is represented by the trajectories of discourses in time, in a dynamical space called the discursive space, which has an arbitrary number of dimensions. Given space is used to represent a given part of knowledge. A manifold is introduced to represent knowledge with a wider scope (all knowledge). The strong version is an extrapolation of the weak version to cover all forms of knowledge, not necessarily human or manifesting in language. The use of dynamical space construction allows one to formalize knowledge as such. Such an effort requires us to initially consider knowledge as mainly a social and linguistic phenomenon, which also could be presented as a result of the evolution of the understanding of knowledge that took place in the 20th century.

Organization of a Latent Space structure in VAE/GAN trained by navigation data

We present a novel artificial cognitive mapping system using generative deep neural networks, called variational autoencoder/generative adversarial network (VAE/GAN), which can map input images to latent vectors and generate temporal sequences internally. The results show that the distance of the predicted image is reflected in the distance of the corresponding latent vector after training. This indicates that the latent space is self-organized to reflect the proximity structure of the dataset and may provide a mechanism through which many aspects of cognition are spatially represented. The present study allows the network to internally generate temporal sequences that are analogous to the hippocampal replay/pre-play ability, where VAE produces only near-accurate replays of past experiences, but by introducing GANs, the generated sequences are coupled with instability and novelty.

Toward Affective Interactions: E-Motions and Embodied Artificial Cognitive Systems.

Toward Affective Interactions: E-Motions and Embodied Artificial Cognitive Systems.

Para-functional engineering: cognitive challenges

Self-adaptive behavior can be defined as the behavior that allows an agent to adapt to a context using her/his/its resources. The property of being ‘self-adaptive’ implies considering some preliminary sources or elicitors for such skill. In the case of machine learning, all the learning or self-adaptive behavior mechanisms are related to algorithmic models of mathematical nature, while in the case of humans more subtle neurochemical and symbolic processes (logical and linguistic) are present. The purpose of this paper is to offer a theoretical analysis of the basic mechanisms related to learning processes, always oriented towards the creation of artificial cognitive systems which can implement such bioinspired mechanisms. Parafunctionality is the key innovative concept we introduce for applying bioinspired cognition to machine learning exploring a real mechanism still unexplored.

A whole brain probabilistic generative model: Toward realizing cognitive architectures for developmental robots

Building a human-like integrative artificial cognitive system, that is, an artificial general intelligence (AGI), is the holy grail of the artificial intelligence (AI) field. Furthermore, a computational model that enables an artificial system to achieve cognitive development will be an excellent reference for brain and cognitive science. This paper describes an approach to develop a cognitive architecture by integrating elemental cognitive modules to enable the training of the modules as a whole. This approach is based on two ideas: (1) brain-inspired AI, learning human brain architecture to build human-level intelligence, and (2) a probabilistic generative model (PGM)-based cognitive architecture to develop a cognitive system for developmental robots by integrating PGMs. The proposed development framework is called a whole brain PGM (WB-PGM), which differs fundamentally from existing cognitive architectures in that it can learn continuously through a system based on sensory-motor information.In this paper, we describe the rationale for WB-PGM, the current status of PGM-based elemental cognitive modules, their relationship with the human brain, the approach to the integration of the cognitive modules, and future challenges. Our findings can serve as a reference for brain studies. As PGMs describe explicit informational relationships between variables, WB-PGM provides interpretable guidance from computational sciences to brain science. By providing such information, researchers in neuroscience can provide feedback to researchers in AI and robotics on what the current models lack with reference to the brain. Further, it can facilitate collaboration among researchers in neuro-cognitive sciences as well as AI and robotics.

Multi-modal self-adaptation during object recognition in an artificial cognitive system

The cognitive connection between the senses of touch and vision is probably the best-known case of multimodality. Recent discoveries suggest that the mapping between both senses is learned rather than innate. This evidence opens the door to a dynamic multimodality that allows individuals to adaptively develop within their environment. By mimicking this aspect of human learning, we propose a new multimodal mechanism that allows artificial cognitive systems (ACS) to quickly adapt to unforeseen perceptual anomalies generated by the environment or by the system itself. In this context, visual recognition systems have advanced remarkably in recent years thanks to the creation of large-scale datasets together with the advent of deep learning algorithms. However, this has not been the case for the haptic modality, where the lack of two-handed dexterous datasets has limited the ability of learning systems to process the tactile information of human object exploration. This data imbalance hinders the creation of synchronized datasets that would enable the development of multimodality in ACS during object exploration. In this work, we use a multimodal dataset recently generated from tactile sensors placed on a collection of objects that capture haptic data from human manipulation, together with the corresponding visual counterpart. Using this data, we create a multimodal learning transfer mechanism capable of both detecting sudden and permanent anomalies in the visual channel and maintaining visual object recognition performance by retraining the visual mode for a few minutes using haptic information. Our proposal for perceptual awareness and self-adaptation is of noteworthy relevance as can be applied by any system that satisfies two very generic conditions: it can classify each mode independently and is provided with a synchronized multimodal data set.