Name
Abstract | ||
|---|---|---|
An intelligent agent should be able to show different emotional behaviours in different interaction situations to become believable and establish close relationships with human counterparts. It is widely accepted that personality and mood play an important role in modulating emotions. However, current computational accounts of emotion for intelligent agents do not effectively integrate the notions of personality and mood in the process of emotion generation. Previous attempts that have been made are mostly based on the assumptions of the researcher, rather than on empirical data and scientific validation. In this paper, we present the results of a novel supervised machine learning approach used to train a network of emotions that integrates the factors of personality and mood, which provides a high emotion intensity prediction accuracy. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.5555/3306127.3332039 | adaptive agents and multi-agents systems |
Field | DocType | Citations |
Mood,Intelligent agent,Computer science,Cognitive psychology,Artificial intelligence,Machine learning,Personality | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Suman Ojha | 1 | 12 | 3.51 |
| Jonathan Vitale | 2 | 21 | 7.17 |
| Syed Ali Raza | 3 | 9 | 6.47 |
| Richard Billingsley | 4 | 3 | 2.08 |
| Mary-Anne Williams | 5 | 0 | 0.68 |