UCD CS academics invited to organise prestigious Dagstuhl seminars
The invitation-only (opens in a new window)Dagstuhl Seminars are highly prestigious and provide a unique format and environment for the exchange and development of ideas. Each seminar focuses on a specific area of computing research. A Dagstuhl seminar involves intense collaboration among top computer science professionals, influencing progress and direction in the science. They are attended by specialist scholars and industry experts from many countries. Seminars take place at the (opens in a new window)Schloss Dagstuhl – Leibniz Center for Informatics in Germany.
(opens in a new window)Professor Mark Keane has been invited to co-organise and present a Dagstuhl Seminar in Germany with international collaborators from France, Netherlands and the USA. The seminar is on (opens in a new window)Explainable AI for Sequential Decision Making. It will take place at (opens in a new window)Schloss Dagstuhl – Leibniz Center for Informatics in Germany over 3 days ( 9-11 September 2024) with approximately 25 participants. Prof. Keane’s collaborators are Hendrik Baier (TU Eindhoven, NL), Sarath Sreedharan (Colorado State University – Fort Collins, USA), Silvia Tulli (Sorbonne University – Paris, FR) and Abhinav Verma (Pennsylvania State University, USA).
As we work with AI and rely on AI for more and more decisions that influence our lives, the research area of explainable AI (XAI) has rapidly developed, with goals such as increasing trust, enhancing collaboration, and enabling transparency in AI. However, to date, the focus of XAI has largely been on explaining the input-output mappings of “black box” models like neural networks, which have been seen as the central problem for the explainability of AI systems. While these models are certainly important, intelligent behaviour often extends over time and needs to be explained and understood as such. The challenge of explaining sequential decision-making (SDM), such as that of robots collaborating with humans or software agents engaged in complex ongoing tasks, has only recently gained attention. The seminar will focus on under-researched challenges that are unique to, or of particular relevance to, explainability in sequential decision-making settings. The aim is to move towards a shared understanding of the field, and to develop a common roadmap for moving it forward.
Assistant Professor Deepak Ajwani is co-organising a seminar on (opens in a new window)Machine Learning Augmented Algorithms for Combinatorial Optimization Problems, with Bistra Dilkina (USC - Los Angeles, US), Tias Guns (KU Leuven, BE), Ulrich Carsten Meyer (Goethe University - Frankfurt am Main, DE). This seminar will take place 27-31 October 2024. The seminar will bring together approximately 45 specialist scholars working at the intersection of algorithm engineering, optimisation solvers and machine learning fields. The proposal to organise this seminar was selected based on a competitive selection process.
Combinatorial optimization problems arise naturally in a multitude of crucial applications, ranging from business analytics, engineering, supply-chain optimisation, transportation, bioinformatics etc. In recent years, motivated by the success of machine learning in diverse fields, researchers have explored if learning techniques can be used to efficiently solve combinatorial optimisation problems. This is challenging because these problems have highly correlated decision variables and the correlations are long-range with very little spatial or temporal coherence. As a result, the end-to-end learning systems that take the problem instance as an input and produce the optimal solution as an output often do not generalise well to instances of larger sizes and from a different input distribution. Experts in this area have advocated for using machine learning in combination with current combinatorial optimisation algorithms to benefit from the theoretical guarantees and state-of-the-art algorithms already available. The discussion in this seminar will focus on how best to combine the machine learning techniques with algorithmic insights and optimisation solvers to solve combinatorial optimisation problem instances more scalably. These discussions are expected to accelerate the pace of research in this area and build collaborations and synergies between the researchers working in the areas of algorithm design and engineering, optimisation solvers and machine learning.
Selected papers from the Seminars will be published in (opens in a new window)Dagstuhl Reports.
The 2024 seminar schedule can be found at this link (opens in a new window)https://www.dagstuhl.de/en/seminars/seminar-calendar
Published 28 March 2024