DeepRAP: Deep Reasoning, Abstraction & Planning towards trustworthy Cognitive AI Systems

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Expected Impact: The resulting portfolio will not only advance the scientific state-of-the-art but also build a robust, interoperable, and application-driven community, positioning Europe at the forefront of trustworthy cognitive AI. It should also lay the foundations for future European leadership in safe, human-centric cognitive AI, supporting sovereignty and competitiveness in key sectors. It will support the ambitions of the AI Act[1] and the European approach to Artificial Intelligence[2]. Expected Outcome: Ambitious proposals put forward under this call will deliver: Models and/or architectures that handle multimodal data and knowledge, uncertainty, and can be trained and deployed with constrained computational resources Provable trustworthiness mechanisms ensuring explainability, transparency, fairness, risk evaluation, security and alignment with ethical and legal standards, including fundamental rights and the EU AI Act, and Demonstrate the developed capabilities integrated in a cognitive AI system (reaching TRL4) performing complex real-world tasks (e.g., scientific discovery, decision support, problem solving) as well as simulations at a scale. In addition, proposals will: Propose new methods and metrics for evaluating and certifying reasoning and trustworthiness in AI as well as the use of the computational resources Follow the FAIR principles ensuring all data, models, and results are Findable, Accessible, Interoperable, and Reusable to maximise transparency, reproducibility, and impact, and Develop synergies with EU initiatives such as TEFs (AI Testing and Experimentation Facilities)[3], eBrains[4], Resource for AI Science in Europe (RAISE)[5], AI-on-demand Platform (AIoD)[6] and the Quantum Flagship[7]. Portfolio approach The composition of the portfolio of projects to be funded under the DeepRAP Challenge will ensure comprehensive coverage across the following categories with a view to ensuring breadth and enabling synergies between the projects: Category 1 – Cognitive Function Capability: Reasoning, abstraction, and planning should be covered by the selected portfolio. Category 2 – Technological Approach: The selected projects are expected to use a variety of technological approaches, including but not limited to, neuro-symbolic AI, deep learning, reinforcement learning, and novel frameworks inspired by interdisciplinary fields, and Category 3 – Use Case and Application Domain: The selected projects will cover a variety of real-world domains, such as industry, mobility, civil security, scientific discovery, health, cybersecurity, justice and human-robot interaction. The selected projects will also be assigned to lead and/or engage in portfolio activities centred on the following priorities: Interoperability: Establishing common standards and protocols to ensure seamless alignment between projects Benchmark Development: Co-creating a DeepRAP benchmark with shared tasks and an open evaluation platform for transparent assessment…