In line with the general trend in artificial intelligence research to create intelligent systems
that combine learning and symbolic components, a new sub-area has emerged that focuses …

In recent years, neural systems have demonstrated highly effective learning ability and
superior perception intelligence. However, they have been found to lack effective reasoning …

Abstract Current advances in Artificial Intelligence (AI) and Machine Learning have achieved
unprecedented impact across research communities and industry. Nevertheless, concerns …

Attempts at combining logic and neural networks into neurosymbolic approaches have been
on the increase in recent years. In a neurosymbolic system, symbolic knowledge assists …

Current high-performance semantic segmentation models are purely data-driven sub-
symbolic approaches and blind to the structured nature of the visual world. This is in stark …

Abstract Automated Planning and Scheduling is among the growing areas in Artificial
Intelligence (AI) where mention of LLMs has gained popularity. Based on a comprehensive …

Neural-symbolic computing (NeSy), which pursues the integration of the symbolic and
statistical paradigms of cognition, has been an active research area of Artificial Intelligence …

The global mental health crisis is looming with a rapid increase in mental disorders, limited
resources, and the social stigma of seeking treatment. As the field of artificial intelligence (AI) …

The research field of end-user programming has largely been concerned with helping non-
experts learn to code sufficiently well in order to achieve their tasks. Generative AI stands to …

The area of neurosymbolic artificial intelligence (Neurosymbolic AI) is rapidly developing
and has become a popular research topic, encompassing subfields, such as neurosymbolic …