Convergence of Artificial Intelligence and Cognitive Systems

This Research Topic focuses on a pioneering area of research that integrates computational intelligence with human-like reasoning, perception, and adaptability. Whereas conventional AI systems are best suited for data-based pattern recognition, they are typically lacking in terms of transparency, reasoning, and flexibility. Cognitive systems, in contrast, model human cognitive processes like decision-making, learning, and problem-solving. Through integration of these paradigms, this research aims to develop more sophisticated AI systems that not only address real-world, complex challenges more effectively but also enhance how AI engages the environment in a human-friendly way. Core industries, such as healthcare, robotics, and autonomous systems, are increasingly drawing on AI technology that can see and learn to adapt to the environment. The combination of AI and cognitive systems has the promise to improve the capabilities of machines while making AI systems not just more efficient, but also more reliable, comprehensible, and inclusive. The convergence of these disciplines provides avenues towards developing ethical and trustworthy applications in various industries, such that AI systems are aligned with human values and societal requirements.

This article collection seeks to fill the gap between theoretical progress in AI and cognitive systems and concrete, real-world applications. Impacts expected are better delivery of healthcare using AI-based personalized treatment systems, stronger decision-making powers in dynamic settings, and creating cognitive digital twins for use in smart cities and industrial automation. This Research Topic is committed to making real-world contributions by pushing explainable AI forward to generate trust and inclusivity, solving AI fairness and robustness to reduce biases, and augmenting AI-augmented creativity to increase human innovation. Through the integration of cutting-edge research, this issue hopes to provide solutions that can bridge the theoretical AI development gap and their practical applications, promoting the creation of adaptive, ethical, and transparent AI systems.

This Research Topic explores key advancements at the intersection of AI and cognitive systems, emphasizing practical applications that demonstrate the power of combining machine learning and human-like reasoning. We invite submissions in the following areas:

• Neurosymbolic AI: Investigating the combination of symbolic reasoning and neural networks to enhance AI's reasoning capabilities while leveraging the strengths of deep learning.
• Explainable AI (XAI): Development of AI systems that are transparent and interpretable, fostering trust in AI-driven decision-making.
• Cognitive Robotics: Exploring the integration of cognitive systems into robotics to enable adaptive, autonomous operations in complex, real-world environments.
• Trustworthy AI and Fairness: Addressing biases, robustness, and ethical concerns beyond human-centric AI, ensuring AI systems are fair, accountable, and resilient.
• Cognitive Digital Twins: Developing AI-driven simulations of human cognitive processes for use in industries such as healthcare, smart cities, and industrial automation.
• AI-Augmented Creativity and Decision-Making: Understanding how AI can complement human cognition in areas such as problem-solving, creative thinking, and innovation, leading to better decision-making processes.
• Intelligent Decision-Making: The design and implementation of reasoning-based AI systems that support real-time decision-making in critical applications.
• Knowledge Representation and Reasoning: Novel approaches for structuring and leveraging knowledge within AI systems to enhance logical inference and real-world problem-solving.
• Applications in Healthcare and Education: Investigating the impact of cognitive AI on healthcare, improving personalized medicine, and exploring AI's role in personalized education and learning.