Automated Playtesting in Collectible Card Games using Evolutionary Algorithms: a Case Study in HearthStone

Collectible card games have been among the most popular and profitable products of the entertainment industry since the early days of Magic: The GatheringTM in the nineties. Digital versions have also appeared, with HearthStone: Heroes of WarCraftTM being one of the most popular. In Hearthstone, every player can play as a hero, from a set of nine, and build his/her deck before the game from a big pool of available cards, including both neutral and hero-specific cards. This kind of games offers several challenges for researchers in artificial intelligence since they involve hidden information, unpredictable behaviour, and a large and rugged search space. Besides, an important part of player engagement in such games is a periodical input of new cards in the system, which mainly opens the door to new strategies for the players. Playtesting is the method used to check the new card sets for possible design flaws, and it is usually performed manually or via exhaustive search; in the case of Hearthstone, such test plays must take into account the chosen hero, with its specific kind of cards. In this paper, we present a novel idea to improve and accelerate the playtesting process, systematically exploring the space of possible decks using an Evolutionary Algorithm (EA). This EA creates HearthStone decks which are then played by an AI versus established human-designed decks. Since the space of possible combinations that are play-tested is huge, search through the space of possible decks has been shortened via a new heuristic mutation operator, which is based on the behaviour of human players modifying their decks. Results show the viability of our method for exploring the space of possible decks and automating the play-testing phase of game design. The resulting decks, that have been examined for balancedness by an expert player, outperform human-made ones when played by the AI; the introduction of the new heuristic operator helps to improve the obtained solutions, and basing the study on the whole set of heroes shows its validity through the whole range of decks

Optimisation of Multilayer Perceptrons Using a Distributed Evolutionary Algorithm with SOAP

Abstract. SOAP (simple object access protocol) is a protocol that al-lows the access to remote objects independently of the computer archi-tecture and the language. A client using SOAP can send or receive ob-jects, or access remote object methods. Unlike other remote procedure call methods, like XML-RPC or RMI, SOAP can use many different transport types (for instance, it could be called as a CGI or as sockets). In this paper an approach to evolutionary distributed optimisation of multilayer perceptrons (MLP) using SOAP and language Perl has been done. Obtained results show that the parallel version of the developed programs obtains similar or better results using much less time than the sequential version, obtaining a good speedup. Also it can be shown that obtained results are better than those obtained by other authors using different methods.