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A Profile-Based ‘GrEvolutionary’ Hearthstone Agent

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Applications of Evolutionary Computation (EvoApplications 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12694))

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Abstract

In the last few years, the Hearthstone AI international Competition has been gaining fame among the scientific community. Several different entries have been presented using varied approaches. One of the best, EVA, was based on a Greedy approach combined with an Evolutionary Algorithm. However, almost all the proposals were designed to work in a general way, i.e. for any of the possible heroes. This generalisation presents a flaw, since the exclusive cards per hero are not really exploited, nor their potential different behaviour profiles. This paper follows a similar philosophy to EVA, also hybridizing Greedy + Evolutionary algorithms, but having in mind three different, and extended among the community, archetypes or profiles: Aggro, Control and Midrange. Thus, three different behaviours have been optimized aiming to create a more specialized agent able to use an Artificial Intelligence engine depending on the hero to play with. To prove the value of the approach several experiments have been conducted, comparing the evolved agents with EVA in many different matches using three different heroes. The results show an improvement over EVA for the three profile-based agents, as well as an excellent performance when combined with a MonteCarlo Tree Search approach.

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Acknowledgements

This work has been supported in part by projects B-TIC-402-UGR18 (FEDER and Junta de Andalucía), RTI2018–102002-A-I00 (Ministerio Español de Ciencia, Innovación y Universidades), and project TIN2017–85727-C4-{1–2}-P (Ministerio Español de Economía y Competitividad).

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Authors and Affiliations

  1. Department Signal Theory, Telematics and Communications, University of Granada, Granada, Spain

    Alejandro Romero García & Antonio M. Mora García

Authors
  1. Alejandro Romero García
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  2. Antonio M. Mora García
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Correspondence to Antonio M. Mora García .

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Editors and Affiliations

  1. ETSIIT-CITIC, University of Granada, Granada, Spain

    Pedro A. Castillo

  2. Université Le Havre Normandie, Le Havre, France

    Juan Luis Jiménez Laredo

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García, A.R., García, A.M.M. (2021). A Profile-Based ‘GrEvolutionary’ Hearthstone Agent. In: Castillo, P.A., Jiménez Laredo, J.L. (eds) Applications of Evolutionary Computation. EvoApplications 2021. Lecture Notes in Computer Science(), vol 12694. Springer, Cham. https://doi.org/10.1007/978-3-030-72699-7_22

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  • DOI: https://doi.org/10.1007/978-3-030-72699-7_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72698-0

  • Online ISBN: 978-3-030-72699-7

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