HearthBot: An Autonomous Agent Based on Fuzzy ART Adaptive Neural Networks for the Digital Collectible Card Game HearthStone
| Autor: | Alysson Ribeiro da Silva, Luís F. W. Góes |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Artificial neural network Heuristic (computer science) business.industry Computer science Deep learning Monte Carlo tree search Autonomous agent 02 engineering and technology Fuzzy logic 020901 industrial engineering & automation Adaptive resonance theory Artificial Intelligence Control and Systems Engineering Adaptive system 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Electrical and Electronic Engineering business Software |
| Zdroj: | IEEE Transactions on Games. 10:170-181 |
| ISSN: | 2475-1510 2475-1502 |
| Popis: | Digital collectible card games, as partially observable games based on alternating turns, such as HearthStone , have been the most played card games in recent years, where the main challenge is the creation of strategies capable of subdue the enemy's moves. From the artificial intelligence perspective, the space of possible strategies is large and dynamic due to the number of cards and actions combinations and also to randomness, which makes the design of efficient autonomous agents a hard problem. This paper presents HearthBot , an autonomous agent that plays HearthStone through an adaptive neural network inspired in the fuzzy adaptive resonance associative map and adaptive resonance theory map. This paper also proposes a new mechanism to categorize and predict information to overcome the overgeneralization problem from those networks. Furthermore, the proposed solution was implemented as a parallel adaptive neural network for a graphics processing unit that achieves a performance compatible with the ones obtained for deep learning methods. HearthBot win rate was evaluated in two experiments playing against a Monte Carlo tree search heuristic with competitive decks on a HearthStone simulator called Metastone . Results show that the proposed solution allows HearthBot to obtain an average win rate performance of 80% against known decks and 70% against unknown decks. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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