Towards a game-independent model and data-structures in digital board games: an overview of the state-of-the-art
Towards a game-independent model and data-structures in digital board games: an overview of the state-of-the-art
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数字棋盘游戏中独立于游戏的模型和数据结构：最新技术概述
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
A. Thapaliya
中科院分区：
文献类型：
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作者：
Luiz Jonatã Pires de Araújo;M. Charikova;J. Sales;V. Smirnov;A. Thapaliya
越来越多的数字棋盘游戏的选择是令人兴奋的，不仅从娱乐的角度来看，而且从学术前景。例如，它可以应用现代计算技术和算法来提取和分析各种游戏的数据，这些游戏包括国际象棋和围棋等经典游戏，以及具有复杂规则的现代棋盘游戏，例如《卡坦岛定居者》和《神秘地》。直观地，不同的数字棋盘游戏需要不同的表示方案和数据结构来保存例如特定时刻的特定游戏的状态或快照。表示模型和数据结构的选择是影响算法解决方案的选择以及人工智能代理的适用性的关键设计决策。这项调查的重点是不同的方案和数据结构，用于表示游戏状态，物理组件，球员，行动和规则的数字棋盘游戏，已在学术文献中报道。这项研究的目的是奠定基础的游戏独立的计算框架，其中包括一个通用的游戏表示，以促进和促进计算技术的应用，例如，人工智能和机器学习这一领域的发展。
The increasing number of options of digital board games is exciting not only from an entertainment perspective but also from an academic prospect. It enables, for example, the application of modern computational techniques and algorithms for extracting and analyzing data from a variety of games - which range from classics like Chess and Go to modern board games with complex rules like Settlers of Catan and Terra Mystica. It is intuitive that different digital board games require distinct representation schemes and data structures to save, for example, the status or snapshot of a particular game at a specific moment. The choice for a representation model and data-structures is a crucial design decision that affects the selection of an algorithmic solution as well as the suitableness for artificial intelligence agents. This survey focuses on the different schemes and data structures used to represent game states, physical components, players, actions and rules for digital board games that have been reported in the academic literature. This study aims to lay the groundwork for the development of a game-independent computational framework which includes a generic game representation to facilitate and promote the application of computational techniques such as, for example, artificial intelligence and machine learning to this domain.