Keynote Lectures

IJCCI is a joint conference composed of three concurrent conferences: ECTA, FCTA and NCTA. These three conferences are always co-located and held in parallel. Keynote lectures are plenary sessions and can be attended by all IJCCI participants.

AI Researchers, Video Games are your Friends!
Julian Togelius, New York University, United States, United States

Scalable Model based Evolutionary Multi-objective Optimization
Yaochu Jin, University of Surrey, United Kingdom, United Kingdom

Perpetual Motion, Evolutionary Computation in Industry and other Chimeras
Anna Esparcia-Alcázar, Universitat Politècnica de València, Spain, Spain

Evolving Fuzzy Systems - Fundamentals, Reliability, Interpretability, Useability and Applications
Edwin Lughofer, Johannes Kepler University, Austria, Austria

AI Researchers, Video Games are your Friends!

Julian Togelius
New York University, United States

Brief Bio
Julian Togelius is Associate Professor in the Department of Computer Science and Engineering, New York University. He studied in Sweden and the UK and worked at universities in Switzerland and Denmark before moving to NYU. Julian's research focuses on AI for games and on games for AI (unsurprisingly, there is considerable synergy between these fields). He is well-known for his contributions to procedural content generation in games (for which he recently co-wrote the first textbook), automatic game design and general video game playing. He is active in organizing the CI/AI in games community, having chaired several of its conferences as well as the IEEE Computational Intelligence Society Games Technical Committee. In order to provide reliable game-based benchmarks for the community, he co-initiated several competitions that test the capabilities of AI methods, including the Simulated Car Racing Championship, the Mario AI Competition and the General Video Game Playing Competition. Several talented and competent individuals seem to like to work with Julian, which allows him to actually implement a fair number of his ideas; this has helped him and his team to score a handful of best paper awards at IEEE-, ACM- and AAAI-sponsored conferences.

Abstract
Artificial intelligence and games go way back. At least to Turing, who re-invented the Minimax algorithm to play Chess even before he had a computer, and to Samuel, who invented a predecessor of TD-learning in order to build a Checkers-playing program in the 1950s. Games are important for AI because they are designed to challenge and train human cognitive capabilities, and are thus uniquely relevant benchmark problems. They are also uniquely convenient benchmark problems, as they allow unbiased comparison between algorithms and can be executed thousands of times fast than realtime. But one should also not forget the financial clout of the games industry and games' appeal for new students.
While research on board games such as Chess and Go has been part of AI research since its inception, the last decade has seen the rise of a research community around AI for video games, and not only for playing them. In this talk I will outline some of the most important trends in recent years. One is General Video Game Playing: developing controllers that can learn to play not just a single game, but a large variety variety of them. Another is Procedural Content Generation, where AI algorithms are used to generate content for games, or even design the games themselves. Yet another trend is AI-assisted design tools, which provide the game designer with instant feedback and suggestions and thus scaffolds the game design process. These research topics inform each other, with general video game playing algorithms being important for procedural content generation and AI-assisted design tools. Finally, I will try to convince you that your own research is important for this endeavor and that you should consider steering your research towards AI for games.

Scalable Model based Evolutionary Multi-objective Optimization

Yaochu Jin
University of Surrey, United Kingdom

Brief Bio
Yaochu Jin (Fellow, 2016) is currently a Distinguished Chair, Professor in Computational Intelligence, Department of Computer Science, University of Surrey, Guildford, U.K., where he heads the Nature Inspired Computing and Engineering Group. He was a “Finland Distinguished Professor” of University of Jyvaskyla, Finland, a “Changjiang Distinguished Visiting Professor”, Northeastern University, China, and “Distinguished Visiting Scholar”, University of Technology Sydney, Australia. His main research interests include data-driven evolutionary optimization, evolutionary learning, trustworthy machine learning, and morphogenetic self-organizing systems. Dr Jin is presently the Editor-in-Chief of the IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS and the Editor-in-Chief of Complex & Intelligent Systems. He was an IEEE Distinguished Lecturer and Vice President for Technical Activities of the IEEE Computational Intelligence Society. He was the General Co-Chair of the 2016 IEEE Symposium Series on Computational Intelligence and the Chair of the 2020 IEEE Congress on Evolutionary Computation. He is the recipient of the 2018 and 2021 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, the 2015, 2017, and 2020 IEEE Computational Intelligence Magazine Outstanding Paper Award, and the Best Paper Award of the 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. He was named by the Web of Science as “a Highly Cited Researcher” in 2019 and 2020. He is a Fellow of IEEE.

Abstract
This talk presents some recent advances in model-based evolutionary multi-objective optimization. We first present a regularity based estimation of distribution algorithm that uses a probabilistic model containing a principal curve and a local Gaussian model. We show that the proposed algorithm is able to work efficiently for large dimensional optimization problems with a small population size. Then, a new evolutionary multi-objective optimization algorithm by using inversed modeling is discussed. By means of constructing inverse models, we are able to sample preferred solutions in the objective space, which is proved to be helpful in maintaining the diversity and generating additional solutions at a low computational cost.

Perpetual Motion, Evolutionary Computation in Industry and other Chimeras

Anna Esparcia-Alcázar
Universitat Politècnica de València, Spain

Brief Bio
Dr Anna I Esparcia-Alcázar holds a degree in Electrical Engineering (1993) from the Universidad Politecnica de Valencia (UPV), Spain, and a PhD (1998) from the University of Glasgow, UK. She has 20 years experience in developing and leading R&D projects & teams both in industry and academia. She is currently with the Research Center on Software Production Methods at the UPV. She is actively involved the field of Evolutionary Computation, which she has applied to areas as diverse as logistics and cybersecurity. She has earned the Evostar Award for Outstanding Contribution to Evolutionary Computation in Europe. She is an elect member of the Executive Committee of SIGEVO, the Special Interest Group of the ACM on Genetic and Evolutionary Computation, a Senior Member of the IEEE and a member of the ACM.

Abstract
Can you apply Computational Intelligence in industry? Is there Evolutionary Computation life outside Academia? Will Benson care? I'll try to find answers to these and other questions with a few reflections from my own history.

Evolving Fuzzy Systems - Fundamentals, Reliability, Interpretability, Useability and Applications

Edwin Lughofer
Johannes Kepler University, Austria
http://www.flll.jku.at/staff/edwin/

Brief Bio
Edwin Lughofer received his PhD. degree from the Department of Knowledge-Based Mathematical Systems, Johannes Kepler University Linz, where he is now employed as key researcher. During the past 10-12 years, he has participated in several research projects on European and national level. In this period, he has published around 130 journal and conference papers in the fields of evolving fuzzy systems, machine learning and vision, data stream mining, active learning, classification and clustering, fault detection and diagnosis, condition monitoring as well as human-machine interaction, including a monograph on ’Evolving Fuzzy Systems’ (Springer, Heidelberg) and an edited book on ’Learning in Non-stationary Environments’ (Springer, New York). He is associate editor of the international journals IEEE Transactions on Fuzzy Systems (IEEE press), Evolving Systems (Springer), Information Fusion (Elsevier) and Soft Computing (Springer), the general chair of the IEEE Conference on Evolving and Adaptive Intelligent Systems 2014 and Area chair of the FUZZ-IEEE 2015 conference in Istanbul. He serves as program committee member of several international conferences, and acts as a peer-reviewer for 20+ international journals. In 2006, he received the best paper award at the International Symposium on Evolving Fuzzy Systems, and in 2013 the best paper award at the IFAC conference in Manufacturing Modeling, Management and Control Conference (800 participants). He is currently key researcher in the national K-Project imPACts and the associated PAC network. He serves as key person in the K-Project HOPL and strategic research projects in collaboration with the Linz Center of Mechatronics (LCM)

Abstract
The keynote speech will provide a round picture of the developments and recent advances in the field of evolving fuzzy systems (EFS) achieved during the last decade since their first time appearance at the beginning of this century.Opposed to conventional fuzzy systems, EFS can be learnt from data (streams) on the fly during (fast) on-line processes in an incremental and mostly single-pass manner. They enjoy a flexible model structure that is able to automatically self-evolve and self-adapt to changes in the process, as e.g. caused by system drifts, new operation modes or dynamic environmental conditions. Therefore, they stand for a very emerging topic in the field of soft computing to address modeling problems in nowadays real-world applications with quickly increasing complexity, more and more implying a shift from batch off-line model design phases (as conducted since the 80ties) to permanent on-line (active) model teaching and adaptation cycles. Furthermore, they can be used in the context of on-line data stream mining and incremental extraction of models and knowledge from huge data bases, not being able to be loaded into virtual memory at once. The focus will be placed on the definition of various model architectures used in the context of EFS, providing an overview about the basic learning concepts and listing the most prominent EFS approaches (fundamentals), discussing recent advances towards an improved stability, reliability and useability (guaranteeing robustness and userfriendliness) as well as aspects towards a grown-up interpretability (offering insights into systems‘ characteristics and nature). The speech will be concluded with a summary of real-world applications such as on-line condition monitoring, visual inspection, human-machine interaction, smart sensors, production systems and others, where various EFS approaches have been successfully applied.