Cooperating Context Method: A Contextual Approach To Story Generation And Telling

Keywords

Context; Cooperating context method CCM; Narrative generation; Story telling

Abstract

Oral story telling has become a lost art because social media and technology have come to dominate personal interactions. The concept of creating a computational system capable of creating, modifying and telling a story, all in real time, could be a compelling way to revive this lost art of oral storytelling. It could serve as a source of alternative entertainment for children. The stories could be decomposed into a series of end-to-end contexts faced by the characters in the story as they seek to attain their sometimes conflicting goals. Thus, contextualization of the story, and of the storytelling process, could become advantageous. With that in mind, this paper presents and evaluates a contextual approach called the Cooperating Context Method (CCM) that helps create and convey dynamic stories in real time. These stories can be easily customized by the listener, also in real time, even while the story is already being told. CCM was designed to overcome the limitations found in other contextual approaches during story generation, while still meeting the design criteria selected through an analysis of human storytellers. CCM begins by examining the current situation to create list of tasks. Through a series of algorithms, the list of tasks is narrowed down into two lists of high priority and low priority contexts while removing the irrelevant contexts. The set of context best suited to manage the tasks are selected and the contextual knowledge is utilized to solve the tasks defined. Testing of CCM revealed that it performs as it was intended.

Publication Date

1-1-2017

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

10257 LNAI

Number of Pages

277-287

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/978-3-319-57837-8_22

Socpus ID

85020900164 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85020900164

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