Generate Believable Causal Plots with User Preferences Using Constrained Monte Carlo Tree Search

Von Wun Soo; Chi Mou Lee; Tai Hsun Chen

Generate Believable Causal Plots with User Preferences Using Constrained Monte Carlo Tree Search

Von Wun Soo, Chi Mou Lee, Tai Hsun Chen

National Tsing Hua University

研究成果: 圖書/報告稿件的類型 › 會議稿件 › 同行評審

6 引文斯高帕斯（Scopus）

摘要

We construct a large scale of causal knowledge in term of Fabula elements by extracting causal links from existing common sense ontology ConceptNet5. We design a Constrained Monte Carlo Tree Search (cMCTS) algorithm that allows users to specify positive and negative concepts to appear in the generated stories. cMCTS can find a believable causal story plot. We show the merits by experiments and discuss the remedy strategies in cMCTS that may generate incoherent causal plots.

原文	英語
主出版物標題	Proceedings of the 12th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2016
編輯	Nathan Sturtevant, Brian Magerko
發行者	Association for the Advancement of Artificial Intelligence
頁面	218-224
頁數	7
ISBN（電子）	9781577357728
出版狀態	已出版 - 08 10 2016
對外發佈	是
事件	12th Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2016 - Burlingame, 美國持續時間: 08 10 2016 → 12 10 2016

出版系列

名字	Proceedings - AAAI Artificial Intelligence and Interactive Digital Entertainment Conference, AIIDE
ISSN（列印）	2326-909X
ISSN（電子）	2334-0924

Conference

Conference	12th Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2016
國家/地區	美國
城市	Burlingame
期間	08/10/16 → 12/10/16

文獻附註

Publisher Copyright:
Copyright © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

其它文件與鏈接

Link to publication in Scopus

引用此

Soo, V. W., Lee, C. M., & Chen, T. H. (2016). Generate Believable Causal Plots with User Preferences Using Constrained Monte Carlo Tree Search. 於 N. Sturtevant, & B. Magerko (編輯), Proceedings of the 12th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2016 (頁 218-224). (Proceedings - AAAI Artificial Intelligence and Interactive Digital Entertainment Conference, AIIDE). Association for the Advancement of Artificial Intelligence.

Soo, Von Wun ; Lee, Chi Mou ; Chen, Tai Hsun. / Generate Believable Causal Plots with User Preferences Using Constrained Monte Carlo Tree Search. Proceedings of the 12th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2016. 編輯 / Nathan Sturtevant ; Brian Magerko. Association for the Advancement of Artificial Intelligence, 2016. 頁 218-224 (Proceedings - AAAI Artificial Intelligence and Interactive Digital Entertainment Conference, AIIDE).

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title = "Generate Believable Causal Plots with User Preferences Using Constrained Monte Carlo Tree Search",

abstract = "We construct a large scale of causal knowledge in term of Fabula elements by extracting causal links from existing common sense ontology ConceptNet5. We design a Constrained Monte Carlo Tree Search (cMCTS) algorithm that allows users to specify positive and negative concepts to appear in the generated stories. cMCTS can find a believable causal story plot. We show the merits by experiments and discuss the remedy strategies in cMCTS that may generate incoherent causal plots.",

keywords = "believable story generation, causal story plots, constrained Monte Carlo Tree Search, Fabula elements, user preference",

author = "Soo, {Von Wun} and Lee, {Chi Mou} and Chen, {Tai Hsun}",

note = "Publisher Copyright: Copyright {\textcopyright} 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 12th Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2016 ; Conference date: 08-10-2016 Through 12-10-2016",

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language = "英语",

series = "Proceedings - AAAI Artificial Intelligence and Interactive Digital Entertainment Conference, AIIDE",

publisher = "Association for the Advancement of Artificial Intelligence",

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Soo, VW, Lee, CM & Chen, TH 2016, Generate Believable Causal Plots with User Preferences Using Constrained Monte Carlo Tree Search. 於 N Sturtevant & B Magerko (編輯), Proceedings of the 12th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2016. Proceedings - AAAI Artificial Intelligence and Interactive Digital Entertainment Conference, AIIDE, Association for the Advancement of Artificial Intelligence, 頁 218-224, 12th Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2016, Burlingame, 美國, 08/10/16.

Generate Believable Causal Plots with User Preferences Using Constrained Monte Carlo Tree Search. / Soo, Von Wun; Lee, Chi Mou; Chen, Tai Hsun.
Proceedings of the 12th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2016. 編輯 / Nathan Sturtevant; Brian Magerko. Association for the Advancement of Artificial Intelligence, 2016. p. 218-224 (Proceedings - AAAI Artificial Intelligence and Interactive Digital Entertainment Conference, AIIDE).

研究成果: 圖書/報告稿件的類型 › 會議稿件 › 同行評審

TY - GEN

T1 - Generate Believable Causal Plots with User Preferences Using Constrained Monte Carlo Tree Search

AU - Soo, Von Wun

AU - Lee, Chi Mou

AU - Chen, Tai Hsun

PY - 2016/10/8

Y1 - 2016/10/8

N2 - We construct a large scale of causal knowledge in term of Fabula elements by extracting causal links from existing common sense ontology ConceptNet5. We design a Constrained Monte Carlo Tree Search (cMCTS) algorithm that allows users to specify positive and negative concepts to appear in the generated stories. cMCTS can find a believable causal story plot. We show the merits by experiments and discuss the remedy strategies in cMCTS that may generate incoherent causal plots.

AB - We construct a large scale of causal knowledge in term of Fabula elements by extracting causal links from existing common sense ontology ConceptNet5. We design a Constrained Monte Carlo Tree Search (cMCTS) algorithm that allows users to specify positive and negative concepts to appear in the generated stories. cMCTS can find a believable causal story plot. We show the merits by experiments and discuss the remedy strategies in cMCTS that may generate incoherent causal plots.

KW - believable story generation

KW - causal story plots

KW - constrained Monte Carlo Tree Search

KW - Fabula elements

KW - user preference

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M3 - 会议稿件

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T3 - Proceedings - AAAI Artificial Intelligence and Interactive Digital Entertainment Conference, AIIDE

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BT - Proceedings of the 12th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2016

A2 - Sturtevant, Nathan

A2 - Magerko, Brian

PB - Association for the Advancement of Artificial Intelligence

T2 - 12th Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2016

Y2 - 8 October 2016 through 12 October 2016

ER -

Soo VW, Lee CM, Chen TH. Generate Believable Causal Plots with User Preferences Using Constrained Monte Carlo Tree Search. 於 Sturtevant N, Magerko B, 編輯, Proceedings of the 12th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2016. Association for the Advancement of Artificial Intelligence. 2016. p. 218-224. (Proceedings - AAAI Artificial Intelligence and Interactive Digital Entertainment Conference, AIIDE).

Generate Believable Causal Plots with User Preferences Using Constrained Monte Carlo Tree Search

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出版系列

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