HyPED: Modeling and Analyzing Action Games as Hybrid Systems
HyPED: Modeling and Analyzing Action Games as Hybrid Systems
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HyPED：将动作游戏作为混合系统进行建模和分析
DOI：
10.1609/aiide.v13i1.12937
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发表时间：
2021
影响因子：
22.7
通讯作者：
Michael Mateas
中科院分区：
文献类型：
--
作者：
J. Osborn;Brian Lambrigger;Michael Mateas
平台游戏和动作冒险游戏具有高维状态空间，对角色移动有困难的非线性约束;更糟糕的是，游戏环境通常以复杂的方式响应玩家，这可能导致规划搜索空间的指数级扩展。这些高维空间中的规划问题通常需要特定领域的知识和手动抽象的游戏规则模型，以复制人类设计师或游戏测试人员的直觉。在这项工作中，我们概述了这些复杂的游戏在一个精确的和低水平的混合自动机建模系统。有了这种表示，标准的增量搜索算法可以用来回答可达区域查询，利用嵌入在系统中的域信息。
Platformers and action-adventure games have high-dimensional state spaces with difficult, non-linear constraints on character movement; even worse, game environments often respond to the player in complex ways that can cause exponential expansion of the planning search space. Planning problems in these high-dimensional spaces generally require domain-specific knowledge and manually abstracted models of game rules to replicate the intuition of human designers or playtesters. In this work, we outline a system for modeling these complex games at a precise and low level in terms of hybrid automata. With this representation, standard incremental search algorithms can be used to answer reachable-region queries, taking advantage of the domain information embedded in the system.