Symbolic Reasoning for <italic>Hearthstone</italic>

Abstract

Abstract-Trading-Card-Games are an interesting problem domain for Game AI, as they feature some challenges, such as highly variable game mechanics, that are not encountered in this intensity in many other genres. We present an expert system forming a player-level AI for the digital Trading-Card-Game Hearthstone. The bot uses a symbolic approach with a semantic structure, acting as an ontology, to represent both static descriptions of the game mechanics and dynamic game-state memories. Methods are introduced to reduce the amount of expert knowledge, such as popular moves or strategies, represented in the ontology, as the bot should derive such decisions in a symbolic way from its knowledge base. We narrow down the problem domain, selecting the relevant aspects for a play-to-win bot approach and comparing an ontology-driven approach to other approaches such as machine learning and case-based reasoning. Upon this basis, we describe how the semantic structure is linked with the game-state and how different aspects, such as memories, are encoded. An example will illustrate how the bot, at runtime, uses rules and queries on the semantic structure combined with a simple utility system to do reasoning and strategic planning. Finally, an evaluation is presented that was conducted by fielding the bot against the stock "Expert" AI that Hearthstone is shipped with, as well as Human opponents of various skill levels in order to assess how well the bot plays. Evaluating how believable the bot reasons is assessed through a Pseudo-Turing test.

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“…Esports have been covered extensively in academic literature from its rise as a major form of entertainment to its fit within the defining characteristics of “sport” (Jenny, Manning, Kelper, & Olrich, 2017; Hamari & Sjöblom, 2017; T. L. Taylor, 2012). Hearthstone has received little scholarly attention, most of which has focused on statistical modeling and artificial intelligence (García-Sánchez, Tonda, Mora, Squillero, & Merelo, 2018; Goes et al, 2017; Stiegler, Dahal, Maucher, & Livingstone, 2018) but has not been the focus of any academic articles focusing on community and esports spaces since its launch in early 2014. Its uniqueness as an emerging esport and its rising global popularity demand further analysis.…”

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“…This line of effort should focus on probabilistic model checking to validate game balance and building smart opponents that can "think" like near-peer adversaries. One possible AI method is the Monte Carlo search tree implemented for Hearthstone [28]. This next step will help to validate BSN as an MDO model, create relevant learning opportunities, and reveal the most promising game strategies for deeper analysis regarding real-world strategies.…”

“…8 Cyber Threat Defender, 9 another serious game, also influenced the design. In particular, Hearthstone has been the topic of recent research applying Artificial Intelligence (AI) research to create capable autonomous agents [28] [29].…”

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