Qualitative Physics in Angry Birds

P. Walega, Michał Zawidzki, Tomasz Lechowski
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引用次数: 17

Abstract

In this paper, we present a program designed to successfully and autonomously play Angry Birds, which attempts to embrace motives of human players in their choices of targets they want to shoot at in a game play. The program comprises two modules: the representation module and the reasoning module. In the former, we introduce qualitative space representation that utilizes notions such as “to lie on,” “to lie to the right,” “to be a shelter of a target,” etc. The latter investigates how particular blocks of a structure behave once one of them has been hit. It includes two algorithms, namely vertical impact and horizontal impact. The first one is a novel method of investigating the behavior of complex structures after one of their constituent blocks gets hit. Namely, it predicts which elements of a structure fall if a supporting block gets destroyed. Horizontal impact, on the other hand, simulates force propagation between adjacent elements after one of them gets struck. We also describe experimental tests we have conducted in which Vertical Impact correctly predicted which blocks will fall in over 98% of investigated cases.
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《愤怒的小鸟》中的定性物理
在本文中,我们设计了一个能够成功自主地玩《愤怒的小鸟》的程序,该程序试图在玩家选择游戏中想要射击的目标时融入人类玩家的动机。该程序包括两个模块:表示模块和推理模块。在前者中,我们引入了定性空间表征,利用诸如“躺在”、“向右躺”、“成为目标的庇护所”等概念。后者研究一旦其中一个被击中,结构的特定块会如何表现。它包括两种算法,即垂直冲击和水平冲击。第一个是研究复杂结构的一个组成块被击中后的行为的新方法。也就是说,它预测如果一个支撑块被破坏,结构的哪些元素会倒塌。另一方面,水平冲击模拟了相邻单元中一个被击中后的力传播。我们还描述了我们进行的实验测试,在这些测试中,垂直冲击正确预测了98%以上的调查案例中哪些块会掉下来。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Transactions on Computational Intelligence and AI in Games
IEEE Transactions on Computational Intelligence and AI in Games COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
4.60
自引率
0.00%
发文量
0
审稿时长
>12 weeks
期刊介绍: Cessation. The IEEE Transactions on Computational Intelligence and AI in Games (T-CIAIG) publishes archival journal quality original papers in computational intelligence and related areas in artificial intelligence applied to games, including but not limited to videogames, mathematical games, human–computer interactions in games, and games involving physical objects. Emphasis is placed on the use of these methods to improve performance in and understanding of the dynamics of games, as well as gaining insight into the properties of the methods as applied to games. It also includes using games as a platform for building intelligent embedded agents for the real world. Papers connecting games to all areas of computational intelligence and traditional AI are considered.
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