The integration of machine learning and reasoning is one of the key open questions in AI. This synergy is motivated by the goal of creating AI systems that not only process information with the efficiency and scalability of data-driven machine learning but also engage in knowledge-driven problem-solving and decision-making with the depth and flexibility of human-like reasoning. The integration of the two facets in AI holds the potential of addressing many of the shortcomings of contemporary AI approaches, including the black-box nature and the brittleness of deep learning, and the difficulty to adapt knowledge representation models in the light of new data.

The challenge of the integration calls for approaches that combine knowledge representation and machine reasoning techniques with learning algorithms from the fields of neural, statistical and relational learning, as well as cognitive science. Various communities are addressing it: Inductive Logic Programming (ILP) and Inductive Programming (IP) are proposed as a form of machine learning (ML) that could induce hypotheses (programs) that generalizes given training examples using formal reasoning techniques; Neuro-Symbolic Artificial Intelligence (NeSy)’s goal is to integrate symbolic reasoning with neural networks, which has recently attracted a lot of attention; Statistical Relational Learning and Artificial Intelligence (StarAI) focus on integrating logical and probabilistic reasoning by studying their semantics and making learning/inference more tractable; Cognitive Science and Artificial Intelligence (CogAI) aim at bringing together leading experts in AI and Cognitive Science to investigate areas of fruitful interaction in which human co-operative and communicative skills can be studied and modeled computationally.

Previously, IJCLR served as a joint event for a series of international conferences and workshops in related research fields.Beginning in the year 2024, the Steering Committee, of the International Joint Conference on Learning and Reasoning (IJCLR) has undertaken a strategic initiative to transform the conference from a collection of individual workshops into a unified and integrated academic event. This integration aimed at fostering interdisciplinary collaboration and enhancing the depth and breadth of discussions within the conference. Additionally, IJCLR introduced a unified set of proceedings, establishing a consolidated publication record that serves as an authoritative source for cutting-edge research and developments in the domain of the integration of learning and reasoning in AI.

Within this restructured format, the ILP, AAIP, and CogAI have been seamlessly incorporated into the IJCLR program, reflecting a comprehensive consolidation of these research areas under the IJCLR's overarching framework. In parallel, NeSy and StarAI continue to organize their dedicated conferences and workshops, maintaining a focus on their specific research directions. Despite their independent activities, NeSy and StarAI remain integral to the IJCLR's core mission, representing key pillars within its research agenda. Their significant contribution to the conference is further highlighted by the involvement of area chairs and program committee members from these fields, playing a critical role in steering the conference towards a holistic academic direction and maintaining the highest standards of quality in its scholarly output.