Program:

~~ SESSION 1: 10:55-12:35 ~~

• Generalizing Rules by Random Forest-based Learning Classifier Systems for High-Dimensional Data Mining
  Fumito Uwano, Koji Dobashi, Keiki Takadama, Tim Kovacs
• Applying Accuracy-based LCS to Detecting Anomalous Database Access
  Suin Seo, Sung-Bae Cho
• Invited Talk: How Learning Classifier Systems Can Conquer Important Modern AI Problems (More details)
  Will Browne

~~ SESSION 2: 14:00-15:40 ~~

• EvoNN - A Customizable Evolutionary Neural Network with Heterogenous Activation Functions
  Boris Shabash, Kay Wiese
• XCSR Based on Compressed Input by Deep Neural Network for High Dimensional Data
  Kazuma Matsumoto, Ryo Takano, Takato Tatsumi, Hiroyuki Sato, Tim Kovacs, Keiki Takadama
• Optimizing clustering to promote data diversity when generating an ensemble classifier
  Zohaib Muhammad Jan, Brijesh Verma, Sam Fletcher
• An Algebraic Description of XCS
  David Pätzel, Jörg Hähner

~~ SESSION 3: 16:05-17:45 ~~

• Modulated Clustering Using Integrated Rough Sets and Scatter Search Attribute Reduction
  Abdel-Rahman Hedar, Abdel-Monem Ibrahim, Alaa Abdel-Hakim, Adel Sewisy
• XCS-CR: Determining Accuracy of Classifier by its Collective Reward in Action Set toward Environment with Action Noise
  Takato Tatsumi, Tim Kovacs, Keiki Takadama
• Model Parameter Adaptive Instance-Based Policy Optimization for Episodic Control Tasks of Nonholonomic Systems
  Kyotaro Ohashi, Natsuki Fujiyoshi, Naoki Sakamoto, Youhei Akimoto
• Integrating Anticipatory Classifier Systems with OpenAI Gym
  Norbert Kozłowski, Olgierd Unold

~~ Organized IWLCS Dinner: Time t.b.a. ~~

Abstract

In the research field of Evolutionary Machine Learning (EML), Learning Classifier Systems (LCS) provide a powerful technique which received a huge amount of research attention over nearly four decades. Since John Holland’s formalization of the Genetic Algorithm (GA) and his conceptualization of the first LCS – the Cognitive System 1 (CS-I) – in the 1970’s, the LCS paradigm has broadened greatly into a framework encompassing many algorithmic architectures, knowledge representations, rule discovery mechanisms, credit assignment schemes, and additional integrated heuristics. This specific kind of EML technique bears a great potential of applicability to various problem domains such as behavior modeling, online-control, function approximation, classification, prediction, and data mining. Clearly, these systems uniquely benefit from their adaptability, flexibility, minimal assumptions, and interpretability. The working principle of a LCS is to evolve a set of condition-action agents (each agent can be an IF-THEN rule or realized by more complex models), so-called classifiers, which partition the problem space into smaller subspaces. On this basis, LCS systems are enabled to carry out different kinds of local predictions for the various niches of the problem space. The size and shape of the subspaces each single classifier covers, is optimized via a steady-state Genetic Algorithm (GA) which pursues a globally maximally general subspace, but at the same time strives for maximally accurate local prediction. This principle called “Generalization Hypothesis” was initially formalized by Stewart Wilson in 1995 when he presented the today mostly investigated LCS derivative – the Extended Classifier System (XCS). According to the working principle of LCS/XCS, one could also understand a generic LCS as an Evolving Ensemble of local models which in combination obtain a problem-dependent prediction output. This raises the question: How can we model these classifiers? Or put another way: Which kind of machine learning and evolutionary computation algorithms can be utilized within the well-understood algorithmic structure of a LCS? For example, Artificial Neural Networks (ANN) or Support Vector Machines (SVM) have been used to model classifier predictions. This workshop opens a forum for ongoing research in the field of LCS as well as for the design and implementation of novel LCS-style EML systems, that make use of evolutionary computation techniques to improve the prediction accuracy of the evolved classifiers. Furthermore, it shall solicit researchers of related fields such as (Evolutionary) Machine Learning, (Multi-Objective) Evolutionary Optimization, Neuroevolution, etc. to bring in their experience. In the era of Deep Learning and the recently obtained successes, topics that have been central to LCS for many years, such as human interpretability of the generated models (“Explainable AI”), are now becoming of high interest to other machine learning communities. Hence, this workshop serves as a critical spotlight to disseminate the long experience of LCS in these areas, to attract new interest, and expose the machine learning community to an alternate advantageous modeling paradigm.

Topics

• New approaches for classifier modelling (e.g. ANN, GP, SVM, RBFN,…)
• New means for the partitioning of the problem space (ensemble formation, condition structures, …)
• New ways of classifier mixing (combination of local predictions, ensemble voting schemes)
• Evolutionary Reinforcement Learning (Learning Classifier Systems, Neuroevolution, …)
• Theoretical developments in LCS (behavior, scalability and learning bounds, ...)
• Flexibility of LCS systems regarding types of target problems (single-step/multiple-step reinforcement learning, regression/function approximation, classification, ...)
• Interpretability of evolved knowledge bases (knowledge extraction techniques, visualization approaches, …)
• System enhancements (competent operators, problem structure identification and linkage learning, ...)
• Input encodings / representations (binary, real-valued, oblique, non-linear, fuzzy, ...)
• Paradigms of LCS (Michigan, Pittsburgh, ...)
• LCS for Cognitive Control (architectures, emergent behaviors, ...)
• Applications (data mining, medical domains, bioinformatics, intelligence in games, ...)
• Optimizations and parallel implementations (GPU, matching algorithms, …)
• Evolutionary Rule-Based Machine Learning systems (Artificial Immune Systems, Evolving Fuzzy Rule-based Systems, …)

Organizers

Danilo Vasconcellos Vargas is an Assistant Professor at the Faculty of Information Science and Electrical Engineering, Kyushu University, Japan. He received the B.Eng. degree in computer engineering from the University of São Paulo, São Paulo, Brazil, in 2009, and both the M.Eng. and Ph.D. degree from Kyushu University, Fukuoka, Japan, in 2014 and 2016 respectively. His current research interests focus on general learning systems which include research in evolutionary algorithms, neural networks, Learning Classifier Systems (LCS) and their applications. He has authored more than 18 peer-reviewed papers, some of them in prestigious journals such as Evolutionary Computation (MIT Press) and IEEE Transactions of Neural Networks and Learning Systems. He received prestigious awards and scholarships such as the German “Baden-Württemberg Scholarship” to study in Germany for 4 months, the Japanese “Monbukagakusho Scholarship (MEXT)” to study in Japan for more than five years, the 2016 Excellent Student Award of The IEEE Fukuoka Section, among others. He co-organized the 2017 Twentieth International Workshop on Learning Classifier Systems (IWLCS). Site: http://itslab.csce.kyushu-u.ac.jp/~vargas/.

Masaya Nakata is an assistant professor at the Faculty of Engineering, Yokohama National University. He received the B.A., M.Sc. Ph.D. degrees in informatics from the University of Electro- Communications, Japan, in 2011, 2013, 2016 respectively. He was a visiting student of the School of Engineering and Computer Science in Victoria University of Wellington from 2014. He was a visiting student of the Department of Electronics and Information, Politecnico di Milano, Milan, Italy, in 2013, and of the Department of Computer Science, University of Bristol, Bristol, UK, in 2014. His research interests are in evolutionary computation, reinforcement learning, data mining, more specifically, in learning classifier systems. He has received the best paper award and the IEEE Computational Intelligence Society Japan Chapter Young Researcher Award from the Japanese Symposium of Evolutionary Computation 2012. He is a co-organizer of International Workshop on Learning Classifier Systems (IWLCS) for 2015-2016. Site: http://hamagamilab.ynu.ac.jp/nakata/

Anthony Stein is a research associate and PhD student at the Faculty of Applied Computer Science, University of Augsburg, Germany. He received his BSc in Business Information Systems from the University of Applied Sciences in Augsburg in 2012. Afterward, he went to the University of Augsburg to proceed with his master's degree (MSc) in computer science with a minor in information economics which he received in 2014. Within his master's thesis, he dived into the nature of Learning Classifier Systems for the first time. Since then, he is a passionate follower of ongoing research in this field. Besides his position at the Organic Computing Group at the University of Augsburg, he is working on his PhD thesis in computer science. His research focuses on the applicability of LCS in autonomous self-learning technical systems which are asked to act in real world environments that exhibit challenges such as data imbalance or non-stationarity. Therefore, in his work he makes use of interpolation techniques to change the means how classifiers are initialized or adequate actions are selected. A further research aspect he investigates is the question how learning classifier systems can be enhanced toward proactive knowledge construction. For the second time now, he also co-organizes the Workshop on Self-Optimization in Autonomic and Organic Computing Systems (SAOS). Site: https://www.informatik.uni-augsburg.de/de/lehrstuehle/oc/mitarbeiter/stein/