基于时空推理的对象间学习行为及相互作用

Q2 Computer Science
M. Ersen, Sanem Sariel
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引用次数: 9

摘要

在本文中,我们介绍了一个通过观察事件序列来学习对象行为和交互的自动推理系统。我们使用一个现有的系统来学习对象的模型,并进一步扩展它来模拟更复杂的行为。此外,我们提出了一种基于时空推理的学习方法来推理对象之间的相互作用。通过学习获得的经验将被这些对象用于实现目标。我们以The Incredible Machine (TIM)游戏为主要测试平台来分析我们的系统。游戏教程是用来训练系统的。我们在四种不同的输入类型上分析推理系统的结果:关系知识库;空间信息;时间信息;以及来自环境的时空信息。我们的分析表明,如果提供了一个关于关系的知识库,大多数交互都是可以学习的。我们还证明,我们的学习方法结合了空间和时间信息,其结果与基于知识的方法接近。这是有希望的,因为收集时空信息不需要事先了解关系。我们对电子游戏领域的时空推理方法进行了二次分析,验证了我们方法的成功。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Learning Behaviors of and Interactions Among Objects Through Spatio–Temporal Reasoning
In this paper, we introduce an automated reasoning system for learning object behaviors and interactions through the observation of event sequences. We use an existing system to learn the models of objects and further extend it to model more complex behaviors. Furthermore, we propose a spatio-temporal reasoning based learning method for reasoning about interactions among objects. Experience gained through learning is to be used for achieving goals by these objects. We take The Incredible Machine game (TIM) as the main testbed to analyze our system. Tutorials of the game are used to train the system. We analyze the results of our reasoning system on four different input types: a knowledge base of relations; spatial information; temporal information; and spatio-temporal information from the environment. Our analysis reveals that if a knowledge base about relations is provided, most of the interactions can be learned. We have also demonstrated that our learning method which incorporates both spatial and temporal information gives close results to that of the knowledge-based approach. This is promising as gathering spatio-temporal information does not require prior knowledge about relations. Our second analysis of the spatio-temporal reasoning method in the Electric Box computer game domain verifies the success of our approach.
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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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