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Improving hearthstone AI by learning high-level rollout policies and bucketing chance node events | IEEE Conference Publication | IEEE Xplore

Improving hearthstone AI by learning high-level rollout policies and bucketing chance node events


Abstract:

Modern board, card, and video games are challenging domains for AI research due to their complex game mechanics and large state and action spaces. For instance, in Hearth...Show More

Abstract:

Modern board, card, and video games are challenging domains for AI research due to their complex game mechanics and large state and action spaces. For instance, in Hearthstone - a popular collectible card (CC) (video) game developed by Blizzard Entertainment - two players first construct their own card decks from over 1,000 different cards and then draw and play cards to cast spells, select weapons, and combat minions and the opponent's hero. Players' turns are often comprised of multiple actions, including drawing new cards, which leads to enormous branching factors that pose a problem for state-of-the-art heuristic search methods. In this paper we first present two ideas to tackle this problem, namely by reducing chance node branching factors by bucketing events with similar outcomes, and using high-level policy networks for guiding Monte Carlo Tree Search rollouts. We then apply these ideas to the game of Hearthstone and show significant improvements over a state-of-the-art AI system for this game.
Date of Conference: 22-25 August 2017
Date Added to IEEE Xplore: 26 October 2017
ISBN Information:
Electronic ISSN: 2325-4289
Conference Location: New York, NY, USA

References

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