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General Information
    • ISSN: 2382-6185
    • Abbreviated Title: Int. J. Knowl. Eng.
    • Frequency: Semiyearly
    • DOI: 10.18178/IJKE
    • Editor-in-Chief: Prof. Chen-Huei Chou
    • Executive Editor: Ms. Shira,W.Lu
    • Indexed by: Google Scholar, Crossref, ProQuest
    • E-mail: ijke@ejournal.net
Editor-in-chief
Prof. Chen-Huei Chou
College of Charleston, SC, USA
It is my honor to be the editor-in-chief of IJKE. I will do my best to help develop this journal better.
IJKE 2016 Vol.2(4): 170-176 ISSN: 2382-6185
doi: 10.18178/ijke.2016.2.4.074

Exploratory Approach to the Computational Modeling of Narrative Ability for Artificial Intelligence

Abstract—Narrative ability is an essential element of human intelligence from the perspectives of both psychology and artificial intelligence (AI). It includes many intellectual functions: narrative generation, narrative understanding or interpretation, narrative-mediated communication, and the manipulation of narrativity-based knowledge. The computational modeling of narrative ability is a critical problem in the development of human-like AI agents and user-friendly intelligent information systems. However, implementing this model involves many difficult challenges owing to the structural and phenomenal complexity of narratives. The broader purpose of this study is to develop a computational model of narrative ability from an AI perspective. In this study, we explore the formulation of a conceptual framework of computational narrative ability including a narrativity-based knowledge model and operational modules for this knowledge model.

Index Terms—Artificial intelligence, computational narrative ability, narrative agent, story-form knowledge.

T. Akimoto is with the Graduate School of Informatics and Engineering, the University of Electro-Communications, Tokyo, Japan (e-mail: t8akimo@yahoo.co.jp).

[PDF]

Cite: Taisuke Akimoto, "Exploratory Approach to the Computational Modeling of Narrative Ability for Artificial Intelligence," International Journal of Knowledge Engineering vol. 2, no. 4, pp. 170-176, 2016.

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