Exploitation of Contextual Affect-Sensing and Dynamic Relationship Interpretation

Zhang, Li (2010) Exploitation of Contextual Affect-Sensing and Dynamic Relationship Interpretation. Computers in Entertainment, 8 (3). pp. 1-16. ISSN 1544-3574

Full text not available from this repository. (Request a copy)
Official URL: http://dx.doi.org/10.1145/1902593.1902597


Real-time contextual affect-detection from open-ended text-based dialogue is challenging but essential for the building of effective intelligent user interfaces. In our previous work, an affect-detection component was developed, which was embedded in an intelligent agent interacting with human-controlled characters under the improvisation of loose scenarios. The affect-detection module is capable of detecting 25 basic and complex emotions based on the analysis of pure individual turn-taking input without any contextual inference. In this article, we report developments on equipping the intelligent agent with the abilities of interpreting dynamic inter-relationships between improvisational human-controlled characters and performing contextual affect-sensing, based on the discussion topics, the improvisational “mood” that one has created, relationship interpretation between characters, and the most recent affect profiles of other characters. Evaluation results on the updated affect-detection component are also reported. Overall, the performances of the contextual affect-sensing and dynamic relationship interpretation are promising. The work contributes to the journal themes on affective computing, human-robots/agent interaction, and narrative-based interactive theatre.

Item Type: Article
Subjects: G700 Artificial Intelligence
G900 Others in Mathematical and Computing Sciences
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Sarah Howells
Date Deposited: 12 Sep 2012 14:15
Last Modified: 10 Oct 2019 23:01
URI: http://nrl.northumbria.ac.uk/id/eprint/8793

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics