Zbigniew Michalewicz is the Chief Scientist of Complexica, an Artificial Intelligence software company that helps large organisations sell more products and services, at a higher margin, through the use of automated analytics. He is also Emeritus Professor at the School of Computer Science, University of Adelaide and holds Professor positions at the Institute of Computer Science, Polish Academy of Sciences, at the Polish-Japanese Academy of Information Technology, and an honorary Professor position at State Key Laboratory of Software Engineering of Wuhan University, China. He is also associated with Structural Complexity Laboratory at Seoul National University, South Korea. In December 2013 he was awarded (by the President of Poland, Mr. Bronislaw Komorowski) the Order of the Rebirth of Polish Polonia Restituta - the second highest Polish state decoration civilian (after the Order of the White Eagle), awarded for outstanding achievements in the field of education, science, sports, culture, arts, economy, national defence, social activities, the civil service and the development of good relations with other countries.
For many years his research interests were in the field of evolutionary computation. He published several books, including a monograph Genetic Algorithms + Data Structures = Evolution Programs (3 editions, a few translations, over 18,300 citations, source: Google Scholar), and over 250 technical papers in journals and conference proceedings that are cited widely (over 40,000 citations, source: Google Scholar). He was one of the editors-in-chief of the Handbook of Evolutionary Computation and the general chairman of the First IEEE International Conference on Evolutionary Computation held in Orlando, June 1994.
Zbigniew Michalewicz has over 35 years of academic and industry experiences, and possesses expert knowledge of numerous Artificial Intelligence technologies. He was the co-Founder and Chief Scientist of NuTech Solutions, which was acquired by Netezza and subsequently by IBM, and the co-Founder and Chief Scientist of SolveIT Software, which was acquired by Schneider Electric after becoming the 3rd fastest growing company in Australia. Both companies grew to approximately 200 employees before they were being acquired.
During his time in the corporate world, Professor Michalewicz led numerous large-scale predictive analytics and optimisation projects for major corporations, including Ford Motor Company, BHP Billiton, U.S. Department of Defence, and Bank of America. Professor Michalewicz also served as the Chairman of the Technical Committee on Evolutionary Computation, and later as the Executive Vice President of IEEE Neural Network Council.
Chin-Teng Lin received the B.S. degree from the National Chiao-Tung University (NCTU), Taiwan in 1986, and the Master and Ph.D. degree in electrical engineering from Purdue University, West Lafayette, Indiana, U.S.A. in 1989 and 1992, respectively. He is currently a Distinguished Professor, Co-Director of Centre for AI, and Director of CIBCI Lab, FEIT, UTS. He is also invited as the International Faculty of the University of California at San Diego (UCSD) from 2012 and Honorary Professorship of University of Nottingham from 2014.
Prof. Lin’s research focuses on machine-intelligent systems and brain computer interface, including algorithm development and system design. He has published over 280 journal papers (H-Index 61 based on Google Scholar), and is the co-author of Neural Fuzzy Systems (Prentice-Hall) and author of Neural Fuzzy Control Systems with Structure and Parameter Learning (World Scientific). Dr. Lin served as Editor-in-Chief of IEEE Transactions on Fuzzy Systems from 2011 to 2016, and has served on the Board of Governors of IEEE Circuits and Systems Society, IEEE Systems, Man, and Cybernetics Society, and IEEE Computational Intelligence Society. Dr. Lin is an IEEE Fellow, and received the IEEE Fuzzy Pioneer Award in 2017.
Dr. Wei Gao is currently a Senior Lecturer in the School of Information Management, Victoria University of Wellington. Previously, he held positions as Scientist in Qatar Computing Research Institute, Research Assistant Professor in the Chinese University of Hong Kong, and Research Fellow in the Institute for Infocomm Research in Singapore. His research interests include information retrieval, natural language processing, social media analytics, and artificial intelligence. His publications appear in the major international conferences and journals including ACL, EMNLP, SIGIR, CIKM, WSDM, IJCAI, ACM TOIS, ACM TIST, etc. He is a Senior PC member of AAAI 2019 and has served in the program committees of many other top-tier conferences. He also worked in the organization committees of IJCNLP 2011, ASONAM 2015, BigComp 2016, NLPCC 2015 & 2018. He received PhD from the Chinese University of Hong Kong, and is a member of ACM and Association for Computational Linguistics.
A referring expression in linguistics is any noun phrase identifying an object in a way that will be useful to interlocutors. In the context of knowledge representation and information systems constant symbols occurring in an underlying knowledge base are the artifacts usually used to identify a subset of the objects for which the knowledge base captures knowledge.
This tutorial explores how objects that can be usefully identified can be extended by allowing more general formulas in the underlying language of the knowledge base, called singular referring expressions, to replace constants as syntactic identifiers of such objects. Expanding the possibilities of identifying (possibly implicitly defined) objects serves numerous purposes, ranging from allowing query answers to contain additional tuples (which are typically eliminated due to lack of constant symbols denoting components of such tuples), to answers that are more informative, to decisions on how to communicate references to objects among various cooperating agents, to identification issues related to physical data representation in computer storage (such as relying on addresses in main-memory databases).
Dr. David Toman and Dr. Grant Weddell are professors of Computer Science at the University of Waterloo, Canada. Together with Alexander Borgida (Rutgers), they have introduced referring expressions in the area of Ontology-based data access (OBDA) and received the Ray Reiter Best Paper prize at KR 2016 for this work. They subsequently extended this work to the area of conceptual modelling and other areas connected with ontological reasoning and knowledge representation. They have published and presented results in the area of knowledge representation over the last 20 years at premier AI conferences (including another Reiter prize in 2010); Dr. Toman has also given tutorials in the area of temporal representation and reasoning that led to an invited chapter in the Handbook of Temporal Reasoning in Artificial Intelligence.
Evolutionary algorithms have been used in various ways to create or guide the creation of digital art. In this tutorial we present techniques from the thriving field of biologically inspired art. We show how evolutionary computation methods can be used to enhance artistic creativity and lead to software systems that help users to create artistic work. We start by providing a general introduction into the use of evolutionary computation methods for digital art and highlight different application areas. This covers different evolutionary algorithms including genetic programming for the creation of artistic images. Afterwards, we discuss evolutionary algorithms to create artistic artwork in the context of image transition and animation. We show how the connection between evolutionary computation methods and a professional artistic approach finds application in digital animation and new media art, and discuss the different steps of involving evolutionary algorithms for image transition into the creation of paintings. Afterwards, we give an overview on the use of aesthetic features to evaluate digital art. The feature-based approach complements the existing evaluation through human judgments/analysis and allows to judge digital art in a quantitative way. Finally, we outline directions for future research and discuss some open problems. The tutorial will contain various animations to showcase digital art. We also plan to allow the audience to interact with recent computer systems for digital art.
Dr. Frank Neumann received his diploma and Ph.D. from the Christian-Albrechts-University of Kiel in 2002 and 2006, respectively. He is a professor and leader of the Optimisation and Logistics Group at the School of Computer Science, The University of Adelaide, Australia. Frank has been the general chair of the ACM GECCO 2016. With Kenneth De Jong he organised ACM FOGA 2013 in Adelaide and together with Carsten Witt he has written the textbook "Bioinspired Computation in Combinatorial Optimization - Algorithms and Their Computational Complexity" published by Springer. He is an Associate Editor of the journals "Evolutionary Computation" (MIT Press) and "IEEE Transactions on Evolutionary Computation" (IEEE). In his work, he considers algorithmic approaches in particular for combinatorial and multi-objective optimization problems and focuses on theoretical aspects of evolutionary computation as well as high impact applications in the areas of renewable energy, logistics, and mining.
Aneta Neumann graduated from the Christian-Albrechts-University of Kiel, Germany in computer science and is currently undertaking her postgraduate research at the School of Computer Science, the University of Adelaide, Australia. She was a participant in the SALA 2016 and 2017 exhibitions in Adelaide and has presented invited talks at UCL London, Goldsmiths, University of London, the University of Nottingham, the University of Sheffield, Hasso Plattner Institut Potsdam and Sorbonne University. Aneta is a co-designer and co-lecturer for the EdX Big Data Fundamentals course in the Big Data MicroMasters® program. Her main research interest is understanding the fundamental link between bio-inspired computation and digital art.
Machine learning have been shown to be a powerful technique for intelligent data analytics. Due to the inherent uncertainty associated with the environments, most real-world data are subject to various uncertainty, such as non-stationary data distribution, noisy labels and uncertain misclassification costs, which brought great challenge to machine learning. This talk will introduce how standard machine learning techniques, especially ensemble learning, could be adapted to address the above challenge.
Ke Tang is a Professor at the Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech). Before that, he was with University of Science and Technology of China, first as an Associate Professor (2007-2011) and then a full Professor (2011-2017). His major research interests include evolutionary computation, machine learning and their applications. He has published more than 130 journal and conference papers. He is/was an Associate Editor or Editorial Board Member of the IEEE Trans. on Evolutionary Computation, IEEE Computational Intelligence Magazine, Computational Optimization and Applications (Springer), Natural Computing (Springer) and Memetic Computing (Springer) and served as program/technical chairs/co-chairs of 10 international conferences. He received the Royal Society Newton Advanced Fellowship in 2015 and the 2018 IEEE Computational Intelligence Society Outstanding Early Career Award.
Genetic programming (GP) is an evolutionary computation method with a focus on representations using an arbitrary length data structure. Relaxing the need for an upfront selection of solution size and shape allows GP to explore a rich space of solutions. However, most variants of GP require some form of closure, where each subcomponent of a solution must be freely interchangeable with any other subcomponent. The incorporation of a grammar into GP allows it to maintain a flexible structure for its representation, but also frees GP from its closure requirement and allows GP to factor in domain knowledge and bias.
In this tutorial, we will provide a brief introduction into grammar-guided GP, including the historical development of the method, an overview of its primary operators, and highlight some useful and interesting applications. We will also provide a brief overview of the current trends in grammar-guided GP, centred mainly along the lines of appropriate design of representation and associated search operators.
Dr. Grant Dick and Associate Professor Peter Whigham both hail from the Department of Information Science, University of Otago, where they teach into the data science undergraduate and postgraduate classes. Both have a long track record in genetic programming, with a combined experience of 40 years' research into GP methods. They have a particular interest in theoretical aspects of GP (e.g., the phenomenon of bloat, and fine-grained evaluation of GP fitness) as well as applying GP methods in evolutionary machine learning. Outputs of their work have been published in IEEE Transactions on Evolutionary Computation, Genetic Programming and Evolvable Machines, along with the conferences GECCO and CEC.