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Optimizing Hearthstone Agents using an Evolutionary Algorithm
Neural and Evolutionary Computing · Submitted 25 Oct 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim This outcome shows how evolutionary computation could represent a considerable advantage in developing AIs for collectible card games such as Hearthstone.
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The abstract, sentence by sentence
Digital collectible card games are not only a growing part of the video game industry, but also an interesting research area for the field of computational intelligence. This game genre allows researchers to deal with hidden information, uncertainty and planning, among other aspects. This paper proposes the use of evolutionary algorithms (EAs) to develop agents who play a card game, Hearthstone, by optimizing a data-driven decision-making mechanism that takes into account all the elements currently in play. Agents feature self-learning by means of a competitive coevolutionary training approach, whereby no external sparring element defined by the user is required for the optimization process. One of the agents developed through the proposed approach was runner-up (best 6%) in an international Hearthstone Artificial Intelligence (AI) competition. Our proposal performed remarkably well, even when it faced state-of-the-art techniques that attempted to take into account future game states, such as Monte-Carlo Tree search. This outcome shows how evolutionary computation could represent a considerable advantage in developing AIs for collectible card games such as Hearthstone.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Digital collectible card games are not only a growing part of the video game industry, but also an interesting research area for the field of computational intelligence. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | This game genre allows researchers to deal with hidden information, uncertainty and planning, among other aspects. | background 1.00 | 0.06 | 0.02 | asserted 0.98 |
| s2 | This paper proposes the use of evolutionary algorithms (EAs) to develop agents who play a card game, Hearthstone, by optimizing a data-driven decision-making mechanism that takes into account all the elements currently in play. | contribution 0.91 | 0.80 | 0.09 | asserted 0.89 |
| s3 | Agents feature self-learning by means of a competitive coevolutionary training approach, whereby no external sparring element defined by the user is required for the optimization process. | method 0.97 | 0.83 | 0.05 | asserted 1.00 |
| s4 | One of the agents developed through the proposed approach was runner-up (best 6%) in an international Hearthstone Artificial Intelligence (AI) competition. | result 1.00 | 0.93 | 0.08 | asserted 1.00 |
| s5 | Our proposal performed remarkably well, even when it faced state-of-the-art techniques that attempted to take into account future game states, such as Monte-Carlo Tree search. | result 0.94 | 0.90 | 0.12 | asserted 0.99 |
| s6 | This outcome shows how evolutionary computation could represent a considerable advantage in developing AIs for collectible card games such as Hearthstone. | interpretation 1.00 | 0.65 | 0.88 | hedged possibility 0.94 |
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Original abstract
Digital collectible card games are not only a growing part of the video game industry, but also an interesting research area for the field of computational intelligence. This game genre allows researchers to deal with hidden information, uncertainty and planning, among other aspects. This paper proposes the use of evolutionary algorithms (EAs) to develop agents who play a card game, Hearthstone, by optimizing a data-driven decision-making mechanism that takes into account all the elements currently in play. Agents feature self-learning by means of a competitive coevolutionary training approach, whereby no external sparring element defined by the user is required for the optimization process. One of the agents developed through the proposed approach was runner-up (best 6%) in an international Hearthstone Artificial Intelligence (AI) competition. Our proposal performed remarkably well, even when it faced state-of-the-art techniques that attempted to take into account future game states, such as Monte-Carlo Tree search. This outcome shows how evolutionary computation could represent a considerable advantage in developing AIs for collectible card games such as Hearthstone.
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Cites as background 1 paper
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More information about this event is available in a presentation on the competition's website [ref] and in [ref] .
Introducing the Hearthstone-AI Competition · Hearthstone AI competition results · 0.86
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