Titre : The Cooperating Context Method 

Auteurs : James Hollister, Avelino J. Gonzalez,  

Revue : Modeling and Using Context 

Numéro : Issue 1 

Volume : 2 

Date : 2018/03/7 

DOI : 10.21494/ISTE.OP.2018.0232 

ISSN : 2514-5711 

Résumé : This article presents and evaluates a novel contextual approach called the Cooperating Context Method that can serve to synthesize complete narratives that are interesting and make sense to the human reader/listener. Exis-ting context-driven approaches are generally designed to facilitate the situational awareness of a tactical agent that ope-rates in an interactive world environment (whether physical or virtual). Such agents are generally tasked with making de-cisions in pursuit of their objectives and/or in light of the environment and the actions of other entities in the environment. In effect, these agents live mainly in the present and/or to a much lesser degree, can remember things from the past; they only relate to the future in terms of making plans to achieve their objectives, if any. These existing context-centric ap-proaches are not useful for the creation of a narrative which, by definition, is neither in the past nor the present, but rather in an artificial time frame that covers an arbitrarily long time period. CCM was designed and built to overcome the limita-tions found in other contextual approaches with respect to automated narrative generation. CCM begins to build the nar-rative by examining the current situation to create a list of tasks that are relevant to the situation being faced by the agent. Through a series of algorithms, the list of contexts that are able to perform these tasks is narrowed down to two lists of high priority and low priority contexts, while removing those other contexts deemed irrelevant to the current needs. The set of contexts best suited to manage the tasks are selected and the contextual knowledge is utilized to address the rele-vant tasks. All along this process, the contexts activated and actions taken by all agents (i.e., the characters in the story) are recorded and become part of the emerging narrative. Potential applications of CCM and its ability to build complete narratives include automated story generation, building scenarios for first person shooter video games, and creating si-mulated scenarios for tactical training in first-responder operations and in military operations. The application described in this paper is for automated generation of children’s stories. Extensive testing of CCM revealed that it performs as it was intended. 

Éditeur : ISTE OpenScience