Name
Title | ||
|---|---|---|
Measuring the emotional state among interacting agents: A game theory approach using reinforcement learning. |
Abstract | ||
|---|---|---|
•We suggest a new method for measuring the emotional state among interacting agents.•We employ a non-cooperative game theory approach for represent the interaction.•The Reinforcement Learning process introduces the stimuli to the environment.•For measuring the emotional state it is employed the Kullback–Leibler distance.•We present an application example related to assessment centers. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.eswa.2017.12.036 | Expert Systems with Applications |
Keywords | Field | DocType |
Adaptive autonomous agents,Emotional model,Kullback–Leibler distance,Game theory,Reinforcement learning | Markov model,Computer science,Closeness,Markov chain,Emotion perception,Probability distribution,Artificial intelligence,Game theory,Machine learning,Reinforcement learning,Emotional Problem | Journal |
Volume | ISSN | Citations |
97 | 0957-4174 | 1 |
PageRank | References | Authors |
0.37 | 27 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mireya Salgado | 1 | 1 | 0.37 |
| Julio B. Clempner | 2 | 91 | 20.11 |