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Keynote Lectures

How Theoretical Analyses Can Impact Practical Applications of Evolutionary Computation
Pietro S. Oliveto, University of Sheffield, United Kingdom

Self-organizing Robot Swarms
Marco Dorigo, Université Libre de Bruxelles, Belgium

Swarm Intelligence in Distributed Systems Use-cases
Vesna Sesum-Cavic, TU Vienna, Austria

From Machine Learning to Explainable AI and Beyond
Andreas Holzinger, Medical University Graz, Austria

 

How Theoretical Analyses Can Impact Practical Applications of Evolutionary Computation

Pietro Oliveto
University of Sheffield
United Kingdom
 

Brief Bio
Pietro S. Oliveto is a Senior Lecturer and EPSRC Early Career Fellow at the Department of Computer Sci-ence, University of Sheffield where he leads the ’Rigorous Runtime Analysis of Bio-inspired Computing’ project team, ‘Rigorous Research’ in short. He received the Laurea degree in computer science from the University of Catania, Italy in 2005 and the PhD degree in computational complexity of evolutionary algorithms from the University of Birmingham, UK in 2009. He has been an EPSRC PhD Plus Fellow (2009-2010) and an EPSRC Postdoctoral Fellow in Theo-retical Computer Science (2010-2013) at Birmingham and a Vice-Chancellor’s Fellow (2013-2016) at Shef-field. Dr. Oliveto is Chair of the IEEE Computational Intelligence Society (CIS) Technical Committee for Evolu-tionary Computation (ECTC). He is Associate Editor of IEEE Transactions on Evolutionary Computation (IEEE TEVC) and Editorial Board member of the Algorithms Journal. He has edited special issues of the IEEE TEVC, Evolutionary Computation (ECJ) and Theoretical Computer Science (TCS) journals. He has Chaired the annual Symposium on Foundations of Computational Intelligence (IEEE FOCI) (2014-2020) and is a Steering Committee member of the workshop series on Theory of Randomised Search Heuristics (ThRaSH). He leads the benchmarking Working Group (WG3) of the COST Action CA15140: ‘Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice’. He is a member of the EPSRC Peer Review College and of the Carnegie Trust of Research Assessors for the Universities of Scotland.


Abstract
Rigorous computational complexity analyses of evolutionary algorithms have been performed since the nineties. The first results were inevitably related to simplified algorithms for benchmark problems with significant structures. Nowadays analyses are possible for classical combinatorial optimisation problems with real world applications and for the standard bio-inspired optimisation algorithms that are used in practice. Such analyses shed light on which classes of algorithms should be preferred for different problems and on how to set the algorithmic parameters. In this talk I will provide examples of the insights which can be gained from these theoretical analyses and how they can ultimately lead to better algorithms, parameter settings and results in practice.



 

 

Self-organizing Robot Swarms

Marco Dorigo
Université Libre de Bruxelles
Belgium
 

Brief Bio
Marco Dorigo received his PhD in electronic engineering in 1992 from Politecnico di Milano. From 1992 to 1993, he was a research fellow at the International Computer Science Institute, Berkeley, CA. Since 1993, he is at Université Libre de Bruxelles (ULB) where in 1996 became a tenured researcher of the F.R.S.-FNRS, the Belgian National Funds for Scientific Research. Between June 2011 and December 2014 he was a full professor of computer science at Paderborn University, Germany. He is now co-director of IRIDIA, the artificial intelligence laboratory of the ULB. He is the Editor-in-Chief of Swarm Intelligence, and associate editor or member of the editorial board of many journals on computational intelligence and adaptive systems. Dr. Dorigo is a Fellow of AAAI, EurAI and IEEE. He was awarded the Italian Prize for Artificial Intelligence in 1996, the Marie Curie Excellence Award in 2003, the F.R.S.-FNRS Quinquennal award in applied sciences in 2005, the Cajastur International Prize for Soft Computing in 2007, an ERC Advanced Grant in 2010, the IEEE Frank Rosenblatt Award in 2015, and the IEEE Evolutionary Computation Pioneer Award, in 2016.


Abstract
I will present recent research in swarm robotics where swarms of autonomous robots self-organize to perform tasks that go beyond the capabilities of the single robots in the swarm. white none repeat scroll.
In the talk, I will overview research in swarm robotics done in my research lab, IRIDIA, at the Université Libre de Bruxelles.
I will first present results obtained with homogeneous and heterogeneous swarms of robots that cooperate both physically and logically to perform a number of different tasks.
I will then present recent work on collective decision making, on collective construction, and on the self-organised formation of hierarchical control structures in a robot swarm.



 

 

Swarm Intelligence in Distributed Systems Use-cases

Vesna Sesum-Cavic
TU Vienna
Austria
 

Brief Bio
Vesna Šešum-Cavic is a Senior Scientist and University Lecturer in Computational Intelligence, Institute of Information Systems Engineering, Compilers and Languages Group, Vienna University of Technology, Austria. She is a graduated mathematician (Dipl.Math) and Magistar of Computer Science (Mag.) from University of Belgrade. She received a doctoral degree in Computer Science from Vienna University of Technology (Dr. techn.). Her research interests cover swarm intelligence, network optimization, p2p systems, theory and design of algorithms, combinatorial optimization, complex systems, self-organization, multi-agent systems. She was a conference chair/program committee member of international conferences. Vesna is the current Chair of IEEE Women in Computational Intelligence, and a member of the IEEE Women in Engineering Committee, IEEE CIS Webinars sub-committee and the IEEE CIS Member Activities Committee.


Abstract
The growing complexity of nowadays distributed systems becomes a critical issue.  Distributed software systems are forced to integrate other software systems and components that are often not reliable, exhibit bad performance, and are sometimes unavailable. Such software is typically characterized by a huge problem size concerning number of computers, clients, requests and size of queries, autonomy and heterogeneity of participating organizations, and dynamic changes of the environment. To cope with unforeseen dynamics in the environment and vast number of unpredictable dependencies on participating components, there is a demand on self-organizing approaches.
Swarm intelligence possesses distributive and autonomous properties, and represents a self-organizing biological system.  Therefore, swarm-inspired algorithms play an important role in the design of self-organizing software for distributed systems. Depending on the problem area, an application of swarm-inspired algorithms enables different kinds of self-organization.
In this keynote lecture, an overview of the significance and power of swarm intelligence in coping with some typical distributed systems problems (e.g, information retrieval, load balancing, load clustering, distributed routing) will be presented. It also includes some examples of their implementation and adaptation to these use-cases.



 

 

From Machine Learning to Explainable AI and Beyond

Andreas Holzinger
Medical University Graz
Austria
 

Brief Bio
Andreas Holzinger is lead of the Holzinger Group (Human-Centered AI) at the Medical University Graz and Visiting Professor for explainable AI at the Alberta Machine Intelligence Institute in Edmonton, Canada. Since 2016 he is Visiting Professor for Machine learning in health informatics at Vienna University of Technology. Andreas was Visiting Professor for Machine Learning & Knowledge Extraction in Verona, RWTH Aachen, University College London and Middlesex University London. He serves as consultant for the Canadian, US, UK, Swiss, French, Italian and Dutch governments, for the German Excellence Initiative, and as national expert in the European Commission. Andreas obtained a Ph.D. in Cognitive Science from Graz University in 1998 and his Habilitation in Computer Science from TU Graz in 2003. He founded the Network HCI-KDD to foster a synergistic combination of methodologies of two areas that offer ideal conditions toward unraveling problems in understanding intelligence towards context-adaptive systems: Human-Computer Interaction (HCI) & Knowledge Discovery/Data Mining (KDD), with the goal of supporting human intelligence with artificial intelligence. Andreas is Associate Editor of Springer/Nature Knowledge and Information Systems (KAIS), Section Editor for Machine Learning of Springer/Nature BMC Medical Informatics and Decision Making (MIDM), and Editor-in-Chief of Machine Learning & Knowledge Extraction (MAKE). In his function as Austrian Representative for Artificial Intelligence in IFIP TC 12, he is organizer of the IFIP Cross-Domain Conference “Machine Learning & Knowledge Extraction (CD-MAKE)” and is member of IFIP WG 12.9 Computational Intelligence, the ACM, IEEE, GI, the Austrian Computer Science and the Association for the Advancement of Artificial Intelligence (AAAI). Since 2003 Andreas has participated in leading positions in 30+ R&D multi-national projects, budget 6+ MEUR, 300+ publications, 11k+ citations, h-Index = 47.


Abstract
Explainable AI is rapidly becoming extremely important. Whilst classic rule-based approaches of early AI have been comprehensible "glass-box" approaches at least in narrow domains, their weakness was in dealing with uncertainties of the real world. The introduction of statistical/probabilistic learning methods has made AI increasingly successful. Meanwhile deep learning approaches even exceed human performance in particular tasks. However, such approaches are becoming increasingly opaque, and even if we understand the underlying mathematical principles of such models, they still lack explicit declarative knowledge. For example, words are mapped to high-dimensional vectors, making them unintelligible to humans. What we need in the future are context-adaptive procedures, i.e. systems that construct contextual explanatory models for classes of real-world phenomena. This talk presents some recent approaches on how to understand why a machine decision has been reached, making results re-traceable, explainable and comprehensible on demand and paving the way for an expert in the loop to augment human intelligence with artificial intelligence.



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