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User-centred Design and Development of a Graphical User Interface for Learning Classifier Systems

Authors:

Sebastian von MammenAuthors Info & Claims

GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation

Pages 1830 - 1837

https://doi.org/10.1145/3583133.3596357

Published: 24 July 2023 Publication History

Abstract

This study presents an application that offers an interactive representation of the learning cycle of a learning classifier system (LCS), a rule-based machine learning technique. The research approach utilized a combination of human-computer interaction requirements, design heuristics, and user-centred engineering to scaffold the development of a graphical user interface for an LCS. The evaluation of the application by LCS experts and a novice yielded encouraging results, demonstrating its ability to convey the LCS mechanics to the users. In addition, the experts validated the correctness of the learning cycle. The study's contribution is two-fold: it presents an innovative approach to making the user understand the underlying mechanics of LCS and validates the effectiveness of the developed application. Furthermore, this work sets the stage for future research towards design revision and to further develop the application to accommodate different LCS models and custom data.

References

[1]

M.D. Abdulrahaman, Nasir Faruk, A.A. Oloyede, Nazmat Toyin Surajudeen-Bakinde, Lukman A. Olawoyin, Omenogo Veronica Mejabi, Yusuf Olayinka Imam-Fulani, AbdulGafar Olawale Fahm, and Adesina Lukuman Azeez. 2020. Multimedia tools in the teaching and learning processes: A systematic review. Heliyon 6 (2020).

[2]

Saleema Amershi, Maya Cakmak, William Bradley Knox, and Todd Kulesza. 2014. Power to the people: The role of humans in interactive machine learning. Ai Magazine 35, 4 (2014), 105--120.

Digital Library

[3]

Sooraj K Babu. 2022. A Multi-Layered User Guidance Framework. (2022).

[4]

Sooraj K Babu and Sebastian von Mammen. 2022. Learning Classifier Systems as Recommender Systems. (2022). https://downloads.hci.informatik.uni-wuerzburg.de/BabuVonMammen2022.pdf

[5]

Gérard Biau and Erwan Scornet. 2016. A random forest guided tour. TEST 25, 2 (01 Jun 2016), 197--227.

[6]

Larry Bull, Ester Bernadó-Masilla, and John Holmes. 2008. Learning Classifier Systems in Data Mining. Springer-Verlag.

[7]

Martin V. Butz, Pier Luca Lanzi, and Stewart W. Wilson. 2008. Function Approximation With XCS: Hyperellipsoidal Conditions, Recursive Least Squares, and Compaction. IEEE Transactions on Evolutionary Computation 12, 3 (2008), 355--376.

Digital Library

[8]

Sandra Cairncross and Mike Mannion. 2001. Interactive multimedia and learning: Realizing the benefits. Innovations in education and teaching international 38, 2 (2001), 156--164.

[9]

Angelos Chatzimparmpas, Rafael M Martins, Ilir Jusufi, and Andreas Kerren. 2020. A survey of surveys on the use of visualization for interpreting machine learning models. Information Visualization 19, 3 (2020), 207--233.

[10]

Pyodide contributors and Mozilla. 2021. Pyodide. https://pyodide.org/ Accessed on 05.08.2022.

[11]

Marco Dorigo. 1995. Alecsys and the AutonoMouse: Learning to control a real robot by distributed classifier systems. Machine Learning 19, 3 (01 Jun 1995), 209--240.

[12]

Nugroho Fredivianus. 2009. XCS-RC. https://github.com/nuggfr/xcs-rc-python Accessed on 01.04.2022.

[13]

Cornelia S. Große, Lisa Jungmann, and Rolf Drechsler. 2015. Benefits of illustrations and videos for technical documentations. Comput. Hum. Behav. 45 (2015), 109--120.

Digital Library

[14]

Andreas Halbig, Sooraj K Babu, Shirin Gatter, Marc Erich Latoschik, Kirsten Brukamp, and Sebastian von Mammen. 2022. Opportunities and challenges of virtual reality in healthcare-a domain experts inquiry. Frontiers in Virtual Reality 3 (2022), 837616.

[15]

Michael Heider, David Pätzel, and Alexander RM Wagner. 2022. An overview of LCS research from 2021 to 2022. In Proceedings of the Genetic and Evolutionary Computation Conference Companion. 2086--2094.

Digital Library

[16]

Andreas Hinderks, Martin Schrepp, and Jörg Thomaschewski. [n. d.]. User Experience Questionnaire (UEQ). https://www.ueq-online.org Accessed on 05.08.2022.

[17]

John H. Holland, Lashon B. Booker, Marco Colombetti, Marco Dorigo, David E. Goldberg, Stephanie Forrest, Rick L. Riolo, Robert E. Smith, Pier Luca Lanzi, Wolfgang Stolzmann, and Stewart W. Wilson. 2000. What Is a Learning Classifier System?. In Learning Classifier Systems, From Foundations to Applications. Springer-Verlag, Berlin, Heidelberg, 3--32.

[18]

John H Holland and Judith S Reitman. 1977. Cognitive systems based on adaptive algorithms. Acm Sigart Bulletin 63 (1977), 49--49.

Digital Library

[19]

Anders Krogh. 2008. What are artificial neural networks? Nature Biotechnology 26, 2 (01 Feb 2008), 195--197.

[20]

Pier Luca Lanzi. 2008. Learning classifier systems: then and now. Evolutionary Intelligence 1, 1 (01 Mar 2008), 63--82.

[21]

Bettina Laugwitz, Theo Held, and Martin Schrepp. 2008. Construction and evaluation of a user experience questionnaire. In HCI and Usability for Education and Work: 4th Symposium of the Workgroup Human-Computer Interaction and Usability Engineering of the Austrian Computer Society, USAB 2008, Graz, Austria, November 20--21, 2008. Proceedings 4. Springer, 63--76.

Digital Library

[22]

James R Lewis. 2018. The system usability scale: past, present, and future. International Journal of Human-Computer Interaction 34, 7 (2018), 577--590.

[23]

Rechitha Neevan. 2020. Classifier System. https://youtu.be/_tC_FRrK6IY Accessed on 08.08.2022.

[24]

Jakob Nielsen. 2005. Ten usability heuristics.

[25]

Albert Orriols-Puig, Jorge Casillas, and Ester Bernado-Mansilla. 2009. Fuzzy-UCS: A Michigan-Style Learning Fuzzy-Classifier System for Supervised Learning. IEEE Transactions on Evolutionary Computation 13, 2 (2009), 260--283.

Digital Library

[26]

David Pätzel, Michael Heider, and Alexander R. M. Wagner. 2021. An Overview of LCS Research from 2020 to 2021. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (Lille, France) (GECCO '21). Association for Computing Machinery, New York, NY, USA, 1648--1656.

Digital Library

[27]

Richard Preen and Larry Bull. 2009. Discrete Dynamical Genetic Programming in XCS. In Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation (Montreal, Québec, Canada) (GECCO '09). Association for Computing Machinery, New York, NY, USA, 1299--1306.

Digital Library

[28]

Dominik Sacha, Michael Sedlmair, Leishi Zhang, John A Lee, Jaakko Peltonen, Daniel Weiskopf, Stephen C North, and Daniel A Keim. 2017. What you see is what you can change: Human-centered machine learning by interactive visualization. Neurocomputing 268 (2017), 164--175.

Digital Library

[29]

Dominik Sacha, Michael Sedlmair, Leishi Zhang, John Aldo Lee, Daniel Weiskopf, Stephen North, and Daniel Keim. 2016. Human-centered machine learning through interactive visualization. ESANN.

[30]

Olivier Sigaud and Stewart W. Wilson. 2007. Learning classifier systems: a survey. Soft Computing 11 (2007), 1065--1078.

Digital Library

[31]

Anthony Stein and Masaya Nakata. 2021. Learning Classifier Systems: From Principles to Modern Systems. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (Lille, France) (GECCO '21). Association for Computing Machinery, New York, NY, USA, 498--527.

Digital Library

[32]

Anthony Stein, Dominik Rauh, Sven Tomforde, and Jörg Hähner. 2017. Interpolation in the eXtended Classifier System: An architectural perspective. Journal of Systems Architecture 75 (2017), 79--94.

Digital Library

[33]

Elisabeth Sulmont, Elizabeth Ann Patitsas, and Jeremy R. Cooperstock. 2019. What Is Hard about Teaching Machine Learning to Non-Majors? Insights from Classifying Instructors' Learning Goals. ACM Transactions on Computing Education (TOCE) 19 (2019), 1 -- 16.

Digital Library

[34]

Alistair Sutcliffe and Jan Gulliksen. 2012. User-centered requirements definition. In Usability in Government Systems. Morgan Kaufmann, 285--300.

[35]

Bruce Tognazzini. 2003. First principles of interaction design. Interaction design solutions for the real world, AskTog (2003).

[36]

Ryan Urbanowicz. 2016. Learning Classifier Systems in a Nutshell. https://www.youtube.com/watch?v=CRge_cZ2cJc Accessed on 01.04.2022.

[37]

Ryan Urbanowicz and Ianto Xi. 2018. eLCS. https://github.com/ryanurbs/eLCS Accessed on 01.04.2022.

[38]

Ryan Urbanowicz and Ianto Xi. 2018. ExSTraCS. https://github.com/ryanurbs/ExSTraCS_2.0 Accessed on 01.04.2022.

[39]

Ryan J. Urbanowicz, Gediminas Bertasius, and Jason H. Moore. 2014. An Extended Michigan-Style Learning Classifier System for Flexible Supervised Learning, Classification, and Data Mining. In Parallel Problem Solving from Nature - PPSN XIII, Thomas Bartz-Beielstein, Jürgen Branke, Bogdan Filipič, and Jim Smith (Eds.). Springer International Publishing, Cham, 211--221.

[40]

Ryan J. Urbanowicz and Will N. Browne. 2017. Introduction to Learning Classifier Systems. Springer-Verlag.

Digital Library

[41]

Ryan J Urbanowicz and Jason H Moore. 2009. Learning Classifier Systems: A Complete Introduction, Review, and Roadmap. J. Artif. Evol. App. 2009 (1 2009).

[42]

Ryan J Urbanowicz and Jason H Moore. 2009. Learning classifier systems: a complete introduction, review, and roadmap. Journal of Artificial Evolution and Applications 2009 (2009).

[43]

Ryan J. Urbanowicz and Jason H. Moore. 2015. ExSTraCS 2.0: description and evaluation of a scalable learning classifier system. Evolutionary Intelligence 8 (9 2015), 89--116. Issue 2--3.

[44]

usability.gov. [n. d.]. System Usability Scale (SUS). https://www.usability.gov/how-to-and-tools/methods/system-usability-scale.html Accessed on 05.08.2022.

[45]

Alfredo Vellido. 2020. The importance of interpretability and visualization in machine learning for applications in medicine and health care. Neural computing and applications 32, 24 (2020), 18069--18083.

[46]

Sebastian von Mammen, Andreas Müller, Marc Erich Latoschik, Mario Botsch, Kirsten Brukamp, Carsten Schröder, and Michael Wacker. 2019. VIA VR: A Technology Platform for Virtual Adventures for Healthcare and Well-Being. 1--2. https://downloads.hci.informatik.uni-wuerzburg.de/2019-viavr-vsgames.pdf

[47]

Christiane Gresse von Wangenheim, Jean Carlo Rossa Hauck, Fernando S. Pacheco, and Matheus F. Bertonceli Bueno. 2021. Visual tools for teaching machine learning in K-12: A ten-year systematic mapping. Education and Information Technologies 26 (2021), 5733 -- 5778.

Digital Library

[48]

Christopher J. C. H. Watkins and Peter Dayan. 1992. Q-learning. Machine Learning 8, 3 (01 May 1992), 279--292.

Digital Library

[49]

Stewart Wilson. 1970. ZCS: A zeroth level classifier system. Evolutionary Computation 2 (02 1970).

Digital Library

[50]

Ariel S. Winn, Lisa DelSignore, Carolyn Marcus, Laura Chiel, Eli Freiman, Diane Stafford, and lori Newman. 2019. Applying Cognitive Learning Strategies to Enhance Learning and Retention in Clinical Teaching Settings.

[51]

Peter Xenopoulos, Luis Gustavo Nonato, and Claudio Silva. 2022. Visualization for Machine Learning. In 2022 35th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Vol. 1. 294--301.

[52]

Ying Yang and Tao Chen. 2019. Analysis and visualization implementation of medical big data resource sharing mechanism based on deep learning. IEEE Access 7 (2019), 156077--156088.

[53]

Robert Zhang and Ryan Urbanowicz. 2021. LCS Discovery and Visualization Environment (LCS-Dive). https://github.com/UrbsLab/LCS-Visualization-Pipeline Accessed on 01.04.2022.

Cited By

Domingues IRasteiro D(2024)A Paradigm Shift in Teaching Machine Learning to Sustainable City Management Master StudentsProceedings of the 2024 the 16th International Conference on Education Technology and Computers10.1145/3702163.3702448(413-417)Online publication date: 18-Sep-2024
https://dl.acm.org/doi/10.1145/3702163.3702448
Siddique AHeider MIqbal MShiraishi HLi XHandl J(2024)A Survey on Learning Classifier Systems from 2022 to 2024Proceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3664165(1797-1806)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3664165
Babu SChetitah Mvon Mammen S(2024)Recommender-based User Guidance Framework2024 IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality (AIxVR)10.1109/AIxVR59861.2024.00046(275-280)Online publication date: 17-Jan-2024
https://doi.org/10.1109/AIxVR59861.2024.00046

Index Terms

User-centred Design and Development of a Graphical User Interface for Learning Classifier Systems
1. Computing methodologies
  1. Artificial intelligence
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. Usability testing
      2. User studies

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cover image ACM Conferences

GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation

July 2023

2519 pages

ISBN:9798400701207

DOI:10.1145/3583133

Chair:
Sara Silva,
Program Chair:
Luís Paquete

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 24 July 2023

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GECCO '23 Companion: Companion Conference on Genetic and Evolutionary Computation

July 15 - 19, 2023

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Cited By

Domingues IRasteiro D(2024)A Paradigm Shift in Teaching Machine Learning to Sustainable City Management Master StudentsProceedings of the 2024 the 16th International Conference on Education Technology and Computers10.1145/3702163.3702448(413-417)Online publication date: 18-Sep-2024
https://dl.acm.org/doi/10.1145/3702163.3702448
Siddique AHeider MIqbal MShiraishi HLi XHandl J(2024)A Survey on Learning Classifier Systems from 2022 to 2024Proceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3664165(1797-1806)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3664165
Babu SChetitah Mvon Mammen S(2024)Recommender-based User Guidance Framework2024 IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality (AIxVR)10.1109/AIxVR59861.2024.00046(275-280)Online publication date: 17-Jan-2024
https://doi.org/10.1109/AIxVR59861.2024.00046

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