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Teach me to play, gamer! Imitative learning in computer games via linguistic description of complex phenomena and decision trees

Soft Computing volume 27, pages 3023–3035 (2023)Cite this article

Abstract

In this article, we present a new machine learning model by imitation based on the linguistic description of complex phenomena. The idea consists of, first, capturing the behaviour of human players by creating a computational perception network based on the execution traces of the games and, second, representing it using fuzzy logic (linguistic variables and if-then rules). From this knowledge, a set of data (dataset) is automatically created to generate a learning model based on decision trees. This model will be used later to automatically control the movements of a bot. The result is an artificial agent that mimics the human player. We have implemented, tested and evaluated this technology from two different points of view: performance by using classical metrics (accuracy, ROC area and PRC area) and believability by using a Turing test for trained bots. The results obtained are interesting and promising, showing that this method can be a good alternative to design and implement the behaviour of intelligent agents in video game development.

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Data availability

Enquiries about data availability should be directed to the authors.

References

  • Borovikov I, Harder J, Sadovsky M, Beirami A (2019) Towards interactive training of non-player characters in video games. arXiv preprintarXiv:1906.00535

  • Conde-Clemente P, Alonso JM, Trivino G (2018) Toward automatic generation of linguistic advice for saving energy at home. Soft Comput 22(2):345–359

  • Feng S, Tan A-H (2016) Towards autonomous behavior learning of non-player characters in games. Expert Syst. Appl. 56:89–99

    Article  Google Scholar 

  • Fod A, Matarić MJ, Jenkins OC (2002) Automated derivation of primitives for movement classification. Auton Robots 12(1):39–54

    Article  MATH  Google Scholar 

  • Gemine Q, Safadi F, Fonteneau R, Ernst D (2012) Imitative learning for real-time strategy games. In: 2012 IEEE conference on computational intelligence and games (CIG), IEEE, pp 424–429

  • Marcotte R, Hamilton HJ (2017) Behavior trees for modelling artificial intelligence in games: a tutorial. Comput Games J 6(3):171–184

    Article  Google Scholar 

  • Melendez P (2009) Controlling non-player characters using support vector machines. In: Proceedings of the 2009 conference on future play on@ GDC Canada, pp 33–34

  • Murthy SK (1998) Automatic construction of decision trees from data: a multi-disciplinary survey. Data Min Knowl Discov 2(4):345–389

    Article  Google Scholar 

  • Ngo T (2011) Data mining: practical machine learning tools and technique by Ian H. Witten, Eibe Frank, Mark A. Hell. SIGSOFT Softw Eng Notes 36(5):51–52

    Article  Google Scholar 

  • Rubio-Manzano C, Trivino G (2016) Improving player experience in computer games by using players’ behavior analysis and linguistic descriptions. Int J Human-Comput Stud 95:27–38

    Article  Google Scholar 

  • Rubio-Manzano C, Lermanda Senoceain T, Martinez-Araneda C, Vidal-Castro C, Segura-Navarrete A (2019) Fuzzy linguistic descriptions for execution trace comprehension and their application in an introductory course in artificial intelligence. J Intell Fuzzy Syst 37(6):8397–8415

    Article  Google Scholar 

  • Rubio-Manzano C, Lermanda Senoceain T, Martinez-Araneda C, Vidal-Castro C, Segura-Navarrete A (2018) Human players versus computer games bots: a turing test based on linguistic description of complex phenomena and restricted equivalence functions. In: International conference on information processing and management of uncertainty in knowledge-based systems. Springer, Cham, pp 27–39

  • Thurau C, Paczian T, Sagerer G, Bauckhage C (2007) Bayesian imitation learning in game characters. Int J Intell Syst Technol Appl 2(2–3):284–295

    Google Scholar 

  • Thurau C, Bauckhage C, Sagerer G (2004) Imitation learning at all levels of game-ai. In: Proceedings of the international conference on computer games, artificial intelligence, design and education, 5

  • Thurau C, Bauckhage C, Sagerer G (2004) Learning human-like movement behavior for computer games. In: proceedings of the international conference on the simulation of adaptive behavior, pp 315–323

  • Van Lent M, Laird J (1999) Learning hierarchical performance knowledge by observation. In: ICML, pp 229–238

  • Zadeh LA (2001) A new direction in AI: toward a computational theory of perceptions. AI magazine 22(1):73–73

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Acknowledgements

This paper is the result of work by the SOMOS research group (SOftware-MOdelling-Science), funded by the Universidad del Bío-Bío, Chile. The authors thank the Facultad de Ingeniería de la Universidad Católica de la Santísima Concepción, Chile.

Funding

The authors have not disclosed any funding. Partially supported by the research project DIUBB 170915 2/R by the University of the Bío-Bío.

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Authors and Affiliations

  1. Department of Information Systems, University of the Bío-Bío, Concepción, Chile

    Clemente Rubio-Manzano, Tomás Lermanda, Christian Vidal & Alejandra Segura

  2. University of Católica Santísima Concepción, Concepción, Chile

    Claudia Martínez-Araneda

Authors
  1. Clemente Rubio-Manzano
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  2. Tomás Lermanda
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  3. Claudia Martínez-Araneda
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  4. Christian Vidal
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  5. Alejandra Segura
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Contributions

All authors have contributed equally to the work. All authors contributed to the study conception, design and implementation. Material preparation, data collection and analysis were performed by CR and TL. The first draft of the manuscript was written by CR and TL and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Clemente Rubio-Manzano.

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Conflict of interest

Clemente Rubio, Tomás Lermanda, Claudia Martínez, Christian Vidal and Alejandra Segura declare that they have no conflict of interest.

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Rubio-Manzano, C., Lermanda, T., Martínez-Araneda, C. et al. Teach me to play, gamer! Imitative learning in computer games via linguistic description of complex phenomena and decision trees. Soft Comput 27, 3023–3035 (2023). https://doi.org/10.1007/s00500-022-07476-z

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  • DOI: https://doi.org/10.1007/s00500-022-07476-z

Keywords

  • Imitative learning
  • Machine learning
  • Linguistic description of complex phenomena
  • Computer games
  • Intelligence agents