Name
Title | ||
|---|---|---|
Towards a Model-Learning Approach to Interactive Narrative Intelligence for Opportunistic Storytelling. |
Abstract | ||
|---|---|---|
Opportunistic storytelling is an approach to interactive narrative where game play is the ordinary activity that underlies notable story events, and the AI challenge is to tell a story about what the player is doing, that meets authorial goals. In this preliminary work, we describe a game and AI system that motivates the need for event prediction within the game world, and provides the opportunity for automated machine learning of such a predictive model. We report results showing how different feature models can be learned and compared in this context, towards automating model selection. |
Year | Venue | Field |
|---|---|---|
2016 | ICIDS | Storytelling,Computer science,Narrative inquiry,Game design,Model selection,Interactive narrative,Human–computer interaction,Multimedia,Model learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
3 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Emmett Tomai | 1 | 92 | 12.95 |
| Luis Lopez | 2 | 0 | 1.01 |