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

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, i… more

1. %60
   DIONYSIS GOULARAS

   DoçentYeditepe Üniversitesi

   Shared topics: Dementia and Cognitive Impairment Research

   Overlapping papers: Political News Monitoring and Questioning the Trustworthiness of News among Turkish University Students in the Infodemic COVID-19 Environment (2024) · Predicting the prognosis of MCI patients using longitudinal MRI data (2021)

2. %41
   ESRA ERDEM PATOĞLU

   ProfesörSabancı Üniversitesi

   Shared topics: AI-based Problem Solving and Planning

   Overlapping papers: Human Robot Collaborative Assembly Planning: An Answer Set Programming Approach (2020) · ReAct!: An Interactive Educational Tool for AI Planning for Robotics (2015)

3. %41
   SİNAN SERDAR ÖZKAN

   ProfesörSakarya Uygulamalı Bilimler Üniversitesi

   Shared topics: Robotic Path Planning Algorithms

   Overlapping papers: Design and Structural Analysis of Trailer Sliding Underrun Protection Device Complied with ECE R58.03 Regulation (2022) · Prediction and Control of Product Quality in Deep Drawing Process Design and Implementation of An intelligent System Using Knowledge Technologies (2012)

The fit is computed by code on every request. The call text (title, keywords, start of the official description) is compared with three sources: semantic similarity to the academic profile (50%), shared distinctive terms in YÖKSİS keywords and OpenAlex topics (25%) and publication content — titles, English abstracts and their Turkish translations (25%). Thanks to the translations, Turkish calls also meet papers written in English. Common words weigh little; below 40% is not shown. A subject hint only — not an eligibility, capacity or merit assessment. Hidden profiles are never listed.