Evaluating Planning-Based Experience Managers for Agency and Fun in Text-Based Interactive Narrative
نویسندگان
چکیده
Artificial intelligence (AI) techniques have been applied to video games to make the overall experience more enjoyable. In games with interactive storytelling (IS), player actions can substantially affect plot events and plot characters. Therefore, AI planning techniques have been used to shape the plot in response to player actions that conflict with authorial goals. While such methods are poised to increase player fun and agency, two recent implementations (ASD and PAST) have not been formally evaluated to date. In this paper we do so via a series of user studies for the first time. We show that ASD significantly enhances fun and agency, whereas PAST gets mixed results with an interaction between effects of the experience manager and player prior gaming experience in one user study, and marginally significant results for increased agency in a study with a constrained story domain.
منابع مشابه
A Call for Emotion Modeling in Interactive Storytelling
Artificial Intelligence (AI) techniques are widely used in video games. Recently, AI planning methods have been applied to maintain plot consistency in the face of player’s agency over the narrative. Combined with an automatically populated player model, such AI experience managers can dynamically create a consistent narrative tailored to a specific player. These tools help game narrative desig...
متن کاملProblem Formulation for Accommodation Support in Plan-Based Interactive Narratives
RQ2 What factors affect the relationship of the coherence between two narrative trajectories and a user's experience of agency and fun when accommodating user actions? • Step 1 ○ In A story planning problem ○ Out Sets of character goals that possibly solve problem • Step 2 ○ In Sets of character goals ○ Out Partial story trajectories • Step 3 ○ In Set of partial story trajectories ○ Out Ranked ...
متن کاملOptimizing Player Experience in Interactive Narrative Planning: A Modular Reinforcement Learning Approach
Recent years have witnessed growing interest in data-driven approaches to interactive narrative planning and drama management. Reinforcement learning techniques show particular promise because they can automatically induce and refine models for tailoring game events by optimizing reward functions that explicitly encode interactive narrative experiences’ quality. Due to the inherently subjective...
متن کاملAn Optimal Approach to Local and Global Text Coherence Evaluation Combining Entity-based, Graph-based and Entropy-based Approaches
Text coherence evaluation becomes a vital and lovely task in Natural Language Processing subfields, such as text summarization, question answering, text generation and machine translation. Existing methods like entity-based and graph-based models are engaging with nouns and noun phrases change role in sequential sentences within short part of a text. They even have limitations in global coheren...
متن کاملScheherazade: Crowd-Powered Interactive Narrative Generation
Interactive narrative is a form of storytelling in which users affect a dramatic storyline through actions by assuming the role of characters in a virtual world. This extended abstract outlines the SCHEHERAZADE-IF system, which uses crowdsourcing and artificial intelligence to automatically construct text-based interactive narrative experiences.
متن کامل