Evolving models in Model-Driven Engineering : State-of-the-art and future challenges

The artefacts used in Model-Driven Engineering (MDE) evolve as a matter of course: models are modified and updated as part of the engineering process; metamodels change as a result of domain analysis and standardisation efforts; and the operations applied to models change as engineering requirements change. MDE artefacts are inter-related, and simultaneously constrain each other, making evolution a challenge to manage. We discuss some of the key problems of evolution in MDE, summarise the key state-of-the-art, and look forward to new challenges in research in this area

Synthesising Linear API Usage Examples for API Documentation

International audienc

Model-Driven Simulation-Based Analysis for Multi-Robot Systems

Multi-robot systems are increasingly deployed to provide services and accomplish missions whose complexity or cost is too high for a single robot to achieve on its own. Although multi-robot systems offer increased reliability via redundancy and enable the execution of more challenging missions, engineering these systems is very complex. This complexity affects not only the architecture modelling of the robotic team but also the modelling and analysis of the collaborative intelligence enabling the team to complete its mission. Existing approaches for the development of multi-robot applications do not provide a systematic mechanism for capturing these aspects and assessing the robustness of multi-robot systems. We address this gap by introducing ATLAS, a novel model-driven approach supporting the systematic robustness analysis of multi-robot systems in simulation. The ATLAS domain-specific language enables modelling the architecture of the robotic team and its mission, and facilitates the specification of the team’s intelligence. We evaluate ATLAS and demonstrate its effectiveness on two oceanic exploration missions performed by a team of unmanned underwater vehicles developed using the MOOS-IvP robotic simulator

The IJCAI-23 joint workshop on artificial intelligence safety and safe reinforcement learning (AISafety-SafeRL2023)

We summarize the IJCAI-23 Joint Workshop on Artificial Intelligence Safety and Safe Reinforcement Learning (AISafety-SafeRL2023)1, held at the 32nd International Joint Conference on Artificial Intelligence (IJCAI-23) on August 21-22, 2023 in Macau, China.Conference: IJCAI-23 Joint Workshop on Artificial Intelligence Safety and Safe Reinforcement Learning (AISafety-SafeRL 2023), Macau, China, August 21-22, 2023.</p