Strategic Features and Terrain Generation for Balanced Heroes of Might and Magic III Maps

Kowalski, Jakub; Miernik, Radoslaw; Pytlik, Piotr; Pawlikowski, Maciej; Piecuch, Krzysztof; Sekowski, Jakub

doi:10.1109/cig.2018.8490430

Cited by 5 publications

(3 citation statements)

References 29 publications

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“…Furthermore, current best-known methods of approaching these challenges vary widely based on the game; simple tree searches have shown large improvements in AI performance on LoCM (Klasiński, Meller, and Witkowski 2020;Miernik and Kowalski 2022), but more complex games like Hearthstone require neural networks (Grad 2017) to assist player performance. Some CCGs, such as Hearthstone and Legends of Code and Magic (LoCM) (Kowalski and Miernik 2018), have been designed to play in an entirely digital form (thus reducing some of the logistics required to develop an AI), but these have significantly smaller spaces of card effects when compared to older physical card games such as MtG and Yu-Gi-Oh!. Attempts have been made to digitize MtG via the open-source XMage client, but XMage implementation is large and not straightforward to automate: every single card type has at least one Java class, every possible effect has at least one function call, etc.…”

Section: Procedurementioning

confidence: 99%

Code Generation for Collectible Card Games with Complex APIs

Licato

Fields

Hollis

2023

FLAIRS

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Large pre-trained language models (LMs) such as GPT-3 Codex are able to generate code remarkably well given prompts of natural language text. But if we want to use such LMs to generate code compatible with a specific API or library (e.g., an API which provides the environments in which certain rules, laws, or orders are to be carried out), the amount of computational and data resources required to fine-tune such models can be cost prohibitive to most organizations. Given these practical limitations, is it possible to utilize these massive code-generation LMs to write code compatible with a given API? We develop an algorithm that selects code examples using a smaller LM trained to predict which features of an API are likely to be used in the resulting code, which is a simpler problem than actually generating the code. The selected examples are then used to build a prompt for the larger LM, which in turn generates the final code. We demonstrate our results on a benchmark dataset derived from the collectible card game "Magic: the Gathering," and obtain state-of-the-art results.

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Section: Procedurementioning

confidence: 99%

Code Generation for Collectible Card Games with Complex APIs

Licato

Fields

Hollis

2023

FLAIRS

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“…Games can be fundamentally rebalanced with even small changes to the rules [24], mechanics representation [13] or game content [32]. However, Hom and Marks [22] explore automated approaches to balancing through changing game rules until agents are able to play against each other with relatively equal winrates and few draws.…”

Section: Automated Deckbuilding and Playtestingmentioning

confidence: 99%

Mapping hearthstone deck spaces through MAP-elites with sliding boundaries

Fontaine

Lee

Soros

et al. 2019

Proceedings of the Genetic and Evolutionary Computation Conference

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Quality diversity (QD) algorithms such as MAP-Elites have emerged as a powerful alternative to traditional single-objective optimization methods. They were initially applied to evolutionary robotics problems such as locomotion and maze navigation, but have yet to see widespread application. We argue that these algorithms are perfectly suited to the rich domain of video games, which contains many relevant problems with a multitude of successful strategies and often also multiple dimensions along which solutions can vary.This paper introduces a novel modification of the MAP-Elites algorithm called MAP-Elites with Sliding Boundaries (MESB) and applies it to the design and rebalancing of Hearthstone, a popular collectible card game chosen for its number of multidimensional behavior features relevant to particular styles of play. To avoid overpopulating cells with conflated behaviors, MESB slides the boundaries of cells based on the distribution of evolved individuals. Experiments in this paper demonstrate the performance of MESB in Hearthstone. Results suggest MESB finds diverse ways of playing the game well along the selected behavioral dimensions. Further analysis of the evolved strategies reveals common patterns that recur across behavioral dimensions and explores how MESB can help rebalance the game.

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“…In this paper, we summarize the Strategy Card Game AI Competition (SCGAI) organized since 2019 at IEEE Congress on Evolutionary Computation and IEEE Conference on Games. The competition is based on Legends of Code and Magic (LOCM) [7] programming game, designed especially for fair AI vs. AI matches. LOCM is a small implementation of a CCG, and its advantage over the commercial CCG AI engines is that it is much simpler to handle and thus allows testing more sophisticated algorithms and quickly implementing theoretical ideas.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Approach to Collectible Arena Deckbuilding using Active Card Game Genes

Kowalski

Miernik

2020

2020 IEEE Congress on Evolutionary Computation (CEC)

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This paper concludes five years of AI competitions based on Legends of Code and Magic (LOCM), a small Collectible Card Game (CCG), designed with the goal of supporting research and algorithm development. The game was used in a number of events, including Community Contests on the CodinGame platform, and Strategy Card Game AI Competition at the IEEE Congress on Evolutionary Computation and IEEE Conference on Games. LOCM has been used in a number of publications related to areas such as game tree search algorithms, neural networks, evaluation functions, and CCG deckbuilding.We present the rules of the game, the history of organized competitions, and a listing of the participant and their approaches, as well as some general advice on organizing AI competitions for the research community. Although the COG 2022 edition was announced to be the last one, the game remains available and can be played using an online leaderboard arena.

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Strategic Features and Terrain Generation for Balanced Heroes of Might and Magic III Maps

Cited by 5 publications

References 29 publications

Code Generation for Collectible Card Games with Complex APIs

Code Generation for Collectible Card Games with Complex APIs

Mapping hearthstone deck spaces through MAP-elites with sliding boundaries

Evolutionary Approach to Collectible Arena Deckbuilding using Active Card Game Genes

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