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A Supervised Learning Framework for Modeling Director Agent Strategies in Educational Interactive Narrative | IEEE Journals & Magazine | IEEE Xplore

A Supervised Learning Framework for Modeling Director Agent Strategies in Educational Interactive Narrative


Abstract:

Computational models of interactive narrative offer significant potential for creating educational game experiences that are procedurally tailored to individual players a...Show More

Abstract:

Computational models of interactive narrative offer significant potential for creating educational game experiences that are procedurally tailored to individual players and support learning. A key challenge posed by interactive narrative is devising effective director agent models that dynamically sequence story events according to players' actions and needs. In this paper, we describe a supervised machine-learning framework to model director agent strategies in an educational interactive narrative Crystal Island. Findings from two studies with human participants are reported. The first study utilized a Wizard-of-Oz paradigm where human “wizards” directed participants through Crystal Island's mystery storyline by dynamically controlling narrative events in the game environment. Interaction logs yielded training data for machine learning the conditional probabilities of a dynamic Bayesian network (DBN) model of the human wizards' directorial actions. Results indicate that the DBN model achieved significantly higher precision and recall than naive Bayes and bigram model techniques. In the second study, the DBN director agent model was incorporated into the runtime version of Crystal Island, and its impact on students' narrative-centered learning experiences was investigated. Results indicate that machine-learning director agent strategies from human demonstrations yield models that positively shape players' narrative-centered learning and problem-solving experiences.
Page(s): 203 - 215
Date of Publication: 20 November 2013

ISSN Information:


I. Introduction

Recent years have witnessed substantial growth in research on computational models of interactive narrative in digital games [1]–[5]. Computational models of interactive narrative aim to procedurally adapt story experiences in response to players' actions, as well as tailor story elements to individual players' preferences and needs. A common metaphor for interactive narrative models is a director agent (drama manager), which is a centralized software agent that works behind the scenes to procedurally direct a cast of nonplayer characters and storyworld events [4], [6], [7]. The capacity to augment and revise narrative plans at runtime has shown promise for several applications, including entertainment [8]–[10], art [1], training [11], and education [6], [12], [13]. In education, computational models of interactive narrative have been embedded in narrative-centered learning environments for a range of subjects, including language and culture learning [14], social skills development [12], network security [15], and middle school science [16].

References

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