基于叙事图式的交互式叙事环境目标识别模型研究

Alok Baikadi, Jonathan P. Rowe, Bradford W. Mott, James C. Lester
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引用次数: 2

摘要

目标识别的计算模型对于提高戏剧经理和导演代理的交互式叙事能力具有很大的希望。目标识别的问题,以及它更一般的形式,计划识别,一直是人工智能社区广泛研究的主题。然而,相对而言,很少有人研究互动叙事中的目标识别。在本文中,我们提出了一个研究议程,利用自然语言处理社区启发的叙事结构的显式表示来提高交互式叙事目标识别模型的准确性。我们描述了一种特殊的叙事表现形式,即叙事模式,我们希望它能够有效地捕捉互动叙事中的玩家行为模式,并提高目标识别模型的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Toward Narrative Schema-Based Goal Recognition Models for Interactive Narrative Environments
Computational models for goal recognition hold great promise for enhancing the capabilities of drama managers and director agents for interactive narratives. The problem of goal recognition, and its more general form, plan recognition, have been the subjects of extensive investigation in the AI community. However, relatively little effort has been undertaken to examine goal recognition in interactive narrative. In this paper, we propose a research agenda to improve the accuracy of goal recognition models for interactive narratives using explicit representations of narrative structure inspired by the natural language processing community. We describe a particular category of narrative representations, narrative schemas, that we anticipate will effectively capture patterns of player behavior in interactive narratives and improve the accuracy of goal recognition models.
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