用基于知识的词关联发现解决一个复杂的语言游戏

Q2 Computer Science
Pierpaolo Basile, M. Degemmis, P. Lops, G. Semeraro
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引用次数: 16

摘要

“断头台”是一种语言游戏,其目标是预测一个独特的单词,这个单词以某种方式与五个作为线索的单词联系在一起,这些单词通常彼此无关。人类玩家找到解决方案的能力取决于其文化背景的丰富程度。我们为这款游戏设计了一个人工玩家,基于一个大型知识库(游戏邦注:该知识库利用了网络上的多种资源,如维基百科),为系统提供了理解线索所需的文化和语言背景。系统的“大脑”是一种扩展激活算法,它开始处理线索,在知识库中找到线索与单词之间的关联,并计算出候选解决方案列表。在本文中,我们关注的问题是找到最有希望的候选解作为最终答案。我们通过寻找候选解和线索之间的关联的两种策略改进了传播算法。这些策略允许双向推理,并选择与线索联系最紧密的候选解。实验表明,该系统的性能与普通人类玩家相当。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Solving a Complex Language Game by Using Knowledge-Based Word Associations Discovery
“The Guillotine” is a language game whose goal is to predict the unique word that is linked in some way to five words given as clues, generally unrelated to each other. The ability of the human player to find the solution depends on the richness of her cultural background. We designed an artificial player for that game, based on a large knowledge repository built by exploiting several sources available on the web, such as Wikipedia, that provide the system with the cultural and linguistic background needed to understand clues. The “brain” of the system is a spreading activation algorithm that starts processing clues, finds associations between them and words within the knowledge repository, and computes a list of candidate solutions. In this paper we focus on the problem of finding the most promising candidate solution to be provided as the final answer. We improved the spreading algorithm by means of two strategies for finding associations also between candidate solutions and clues. Those strategies allow bidirectional reasoning and select the candidate solution which is the most connected with the clues. Experiments show that the performance of the system is comparable to that of average human players.
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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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