Project ID: STAI-CDT-2020-IC-39
Themes: AI Planning, Verification
Supervisor: Alessio Lomuscio
In autonomous and multi-agent systems players are normally assumed rational and cooperating and/or competing in groups to achieve their overall objectives. Useful methods to study the resulting interactions come from game...
Read more
Project ID: STAI-CDT-2020-IC-34
Themes: AI Planning, Verification
Supervisor: Alessio Lomuscio
State-of-the-art present perception systems, including those based on Lidar or cameras, are increasingly being used in a range of critical applications including security and autonomous vehicles. While the present deep...
Read more
Project ID: STAI-CDT-2020-KCL-11
Themes: AI Planning, Verification
Supervisor: Steffen Zschaler
Agent-based models (ABMs) are an AI technique to help improve our understanding of complex real-world interactions and their ”emergent behaviour”. ABMs are used to develop and test theories or to explore how...
Read more
Project ID: STAI-CDT-2020-KCL-10
Themes: AI Planning, Verification
Supervisor: Steffen Zschaler
When using complex algorithms to make decisions within autonomous systems, the weak link is the abstract model used by the algorithms: any errors in the model may lead to unanticipated behaviour potentially risking...
Read more
Project ID: STAI-CDT-2020-IC-41
Themes: AI Planning, Verification
Supervisor: Antonio Filieri
Learning and decision-making AI components are gaining popularity as enablers of modern adaptive software. Common uses include, for example, the classification or regression of incoming data (e.g., face recognition), the...
Read more
Project ID: STAI-CDT-2020-IC-42
Themes: AI Planning, Verification
Supervisor: Wayne Luk
Recent intelligent transportation systems reduce traffic congestion and improve the overall efficiency of traffic networks. Such systems often involve complex algorithmic logic, large-scale traffic networks, and a high...
Read more
Project ID: STAI-CDT-2020-IC-43
Themes: AI Planning, Logic
Supervisor: Alessandra Russo
This research proposal focuses on developing a novel hybrid neural-symbolic learning approach that combines the capabilities of deep learning methods in extracting features from unstructured data with the ability of...
Read more
Project ID: STAI-CDT-2020-IC-7
Themes: AI Planning, Verification
Supervisor: Sergio Maffeis
Machine learning (ML) techniques such as Support Vector Machines, Random Forests and Neural Networks are being applied with great success to a wide range of complex and sometimes safety-critical tasks. Recent research in...
Read more
Project ID: STAI-CDT-2020-IC-21
Themes: AI Planning, Logic, Verification
Supervisor: Nobuko Yoshida
SUMMARY Robotics applications involve programming concurrent components synchronising through messages while simultaneously executing motion actions that control the state of the physical world. Today, these applications...
Read more
Project ID: STAI-CDT-2020-KCL-26
Themes: AI Planning, Logic, Verification
Supervisor: Fabio Pierazzi
Intrusion Detection Systems (IDSs) are commonly deployed in networks and hosts to identify malicious activities representing misuse of computer systems. The numbers and types of attacks have been constantly increasing, and...
Read more
Project ID: STAI-CDT-2020-IC-30
Themes: AI Planning, Verification
Supervisor: Herbert Wiklicky
The extraction of (symbolic) rules which describe the operation of (deep) neural networks which have been trained to perform a certain task is central to explaining their inner workings in order to judge their correctness,...
Read more
Project ID: STAI-CDT-2020-IC-31
Themes: AI Planning, Logic, Verification
Supervisor: Alessio Lomuscio
The effective development and deployment of single-robot systems is increasingly shown to be problematic in a variety of application domains including search and rescue, remote exploration, de-mining, etc. These and other...
Read more
