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Bio-Inspired Virtual Populations: Adaptive Behavior with Affective Feedback

Antunes, RF and Magnenat-Thalmann, N.


Abstract: In this paper, we describe an agency model for generative populations of humanoid characters, based upon temporal variation of affective states. We have built on an existing agent framework from Sequeira et al. [18], and adapted it to be susceptible to temperamental and emotive states in the context of cooperative and non-cooperative interactions based on trading activity. More specifically, this model operates within two existing frameworks: a) intrinsically motivated reinforcement learning, structured upon affective appraisals in the relationship of the agents with their environment [20,18]; b) a multi-temporal representation of individual psychology, common in the field of affective computing, structuring individual psychology as a tripartite relationship: emotions-moods-personality [8,16]. Results show a populations of agents that express their individuality and autonomy with a high level of heterogeneous and spontaneous behaviors, while simultaneously adapting and overcoming their perceptual limitations.

Keywords: Artificial Life, Artificial Societies, Crowd Simulation, Emotional Characters, Multi-Agent Frameworks


DOI: 10.1145/2915926.2915929
Link: http://doi.acm.org/10.1145/2915926.2915929
@inproceedings{626,
  author = {Antunes, RF and Magnenat-Thalmann, N.},
  title = {Bio-Inspired Virtual Populations: Adaptive Behavior with Affective Feedback},
  booktitle = {Proceedings of the 29th International Conference on Computer Animation and Social Agents},
  series = {CASA '16},
  year = {2016},
  isbn = {978-1-4503-4745-7},
  location = {Geneva, Switzerland},
  pages = {101--110},
  numpages = {10},
  url = {http://doi.acm.org/10.1145/2915926.2915929},
  doi = {10.1145/2915926.2915929},
  acmid = {2915929},
  publisher = {ACM},
  address = {New York, NY, USA},
  keywords = {Artificial Life, Artificial Societies, Crowd Simulation, Emotional Characters, Multi-Agent Frameworks},
  topic = {Crowd Simulation}
}