Objective:

Innovative ideas put forward under this Challenge must explore novel approaches, including combinations of existing techniques (i.e. neuro-symbolic AI), or the creation of entirely new frameworks that go beyond current, traditional, deep learning and reinforcement learning paradigms. These could be inspired by developments in diverse fields such as neuroscience, biology, physics, philosophy and more.

The proposals should address one or more of the following cognitive capabilities:

·       Deep Reasoning: Moving beyond statistical pattern matching to support causal inference, logical reasoning, and context-aware or commonsense decision-making in complex, unstructured environments. This requires shifting from purely data-driven correlations to AI systems capable of understanding why patterns emerge, identifying underlying causes, and drawing valid conclusions through both deductive and inductive processes. Neuro-symbolic approaches, which combine the learning power of neural networks with the structured inference of symbolic reasoning are particularly encouraged to advance these capabilities. Integrating contextual and commonsense knowledge enables AI to interpret information more holistically, adapt decisions dynamically, and handle ambiguity and uncertainty. Deep reasoning systems should be able to reconcile multiple sources of information, provide transparent and explainable rationales for their outputs, and align with human values and expectations, ensuring trustworthy and accountable operations in demanding real-world scenarios.

·       Deep Abstraction: Enabling AI systems to generalise insights from limited data by forming, manipulating, and refining high-level concepts, analogies, and representations that can be transferred across diverse application domains. This includes the development of internal world models to support abstraction, foster commonsense understanding, and integrate semantic and contextual awareness. Approaches that combine symbolic reasoning, analogical mapping, and representation learning are particularly encouraged, as they empower AI to interpret meaning, intent, and relationships within complex environments. Progress in deep abstraction is essential for achieving cognitive flexibility, robust transfer learning, and adaptive reasoning in dynamic, data-scarce, or rapidly evolving settings.

·       Deep Planning: Developing robust, adaptive, and scalable planning algorithms/models capable of operating in open-world, agentic, or uncertain real-time environments. This involves leveraging advanced deep learning techniques such as deep reinforcement learning and architectures tailored for planning tasks to enable AI systems to autonomously devise, optimise, and adjust complex strategies in dynamic settings. Neuro-symbolic approaches integrating neural networks with symbolic reasoning are particularly encouraged to address uncertainty, provide formal guarantees, and enable explainable, dependable decision-making. Emphasis is placed on long-term, flexible planning that incorporates cognitive timing and predictive modelling, enabling systems to anticipate and adapt within dynamic contexts. Approaches should explore hierarchical planning across multiple temporal levels, contingency planning for effective fallback strategies, and continual re-planning to dynamically update plans as environments evolve. These advancements will underpin resilient, coordinated, and trustworthy AI planning in complex, unpredictable scenarios.

Scope:

Artificial Intelligence (AI) systems have achieved remarkable progress as evidenced by the ability of Generative AI to recognise patterns and generate contextually relevant outputs based on ever larger models and associated datasets. However, despite the remarkable strides made over the past decade, there remains a significant gap between the capabilities of the human brain and machine intelligence, which must be overcome to achieve robust performance and enable effective interactions with users and stakeholders.

Current Generative AI models can release very accurate outputs and even solve some mathematical problems but might struggle with some complex reasoning benchmarks and to understand the real world. These models frequently fail to reliably solve logic tasks and long-term planning, even when provably correct solutions exist, limiting their effectiveness in critical applications where precision is essential.

Inspired by the human brain’s ability to process information at multiple levels of abstraction—enabling perception, reasoning, and goal-directed planning—the goal of this Challenge is to move beyond the current state-of-the-art in traditional AI approaches, whether symbolic (e.g., rules, decision trees, symbolic regression, etc.) or connectionist, neural (e.g., deep learning, large language models, reinforcement learning). The goal is to significantly improve the Reasoning, Abstraction, and Planning (RAP) capabilities of AI systems.

This will overcome the limitations of current deep learning models, which despite their strengths, have limitations in critical cognitive functions for abstraction, contextualisation, causality, explainability, and intelligible reasoning — competencies that are fundamental to move towards human-like intelligence.

The programme is open to:

·       Legal entities from any country

·       Organisations established in EU Member States

·       Organisations from associated countries

·       Eligible legal entities from non-associated third countries

·       International organisations

Applicants must meet the requirements established under:

·       Horizon Europe regulations

·       Specific call conditions