EIC Pathfinder Challenge: DeepRAP — Deep Reasoning, Abstraction and Planning towards trustworthy Cognitive AI
Supports cognitive AI development focused on trustworthy deep reasoning, abstraction, and planning capabilities.
Generative AI can recognise patterns and generate accurate outputs but frequently fails on complex reasoning, long-term planning, and logic tasks where provably correct solutions exist. This Challenge funds approaches that go beyond traditional deep learning and reinforcement learning — including neuro-symbolic combinations and entirely new frameworks inspired by neuroscience, biology, physics, ph…
Significantly improve the Reasoning, Abstraction and Planning (RAP) capabilities of AI systems beyond current symbolic or connectionist paradigms. Proposals must address one or more of: (1) Deep Reasoning (causal inference, logical reasoning, context-aware decisions); (2) Deep Abstraction (generalising from limited data, internal world models, cross-domain transfer); (3) Deep Planning (adaptive, scalable planning in open-world or agentic environments). Neuro-symbolic approaches particularly encouraged. Must demonstrate at TRL 4 and align with the AI Act.
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Guides for this application
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