- Semantic-Based Approaches for Cognitive Robotics:
This theme focuses on developing semantic-based approaches for cognitive robotics. It involves leveraging knowledge graphs and semantic technologies to enhance the cognitive capabilities of robots, enabling them to understand and reason about the environment. - Deep Learning and Big Data for Cognitive Robotics Perception:
This theme explores the application of deep learning techniques and big data analytics for perception tasks in cognitive robotics. It involves developing advanced algorithms and models to improve the perception capabilities of autonomous robots using large-scale datasets. - Knowledge Graph and Deep Learning Techniques for Autonomous Driving:
This theme combines knowledge graph representations and deep learning techniques to address challenges in autonomous driving. It involves developing methods to integrate structured and unstructured data, enabling intelligent decision-making in autonomous vehicles. - Dynamic Decision Making Rules for Autonomous Smart Agents:
This thesis focuses on developing dynamic decision-making rules for autonomous smart agents. It involves designing adaptive decision-making algorithms that can dynamically adjust their behavior based on changing environmental conditions and task requirements. - Extended Reality for Smart Applications:
This thesis explores the use of extended reality in the context of smart applications. It involves the design and the implementation of novel techniques and frameworks to enhance user interaction and visualization in smart environments using extended reality technologies. - Knowledge as a Service for Smart Applications:
This thesis investigates the concept of knowledge as a service in the context of smart applications. It involves designing and implementing knowledge-based systems that can provide on-demand access to relevant information and insights for smart applications. - Reinforcement Learning for Smart Applications:
This theme focuses on applying reinforcement learning techniques to improve the performance of smart applications. It involves developing intelligent algorithms that can learn and adapt to optimize decision-making and resource allocation in smart environments. - Engineering of CAS
This theme focuses on the proposal and experimentation of novel methodologies and technologies for the design, implementation and testing of CAS, with a special focus on autonomous driving systems, cyber physical systems, IoT, and IIOT systems.
