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Open AccessArticle

Smart Sensing: An Info-Structural Model of Cognition for Non-Interacting Agents

1
Department of Computer Science, University of Salerno, 84084 Fisciano, Italy
2
Faculty of Nursing, Vinnytsya Medical College “D. K. Zabolotny”, 21037 Vinnytsya, Ukraine
3
Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milano, Italy
4
Direzione, Sidelmed S.p.A., 84085 Mercato San Severino, Italy
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(10), 1692; https://doi.org/10.3390/electronics9101692
Received: 27 August 2020 / Revised: 2 October 2020 / Accepted: 7 October 2020 / Published: 15 October 2020
(This article belongs to the Section Artificial Intelligence)
This study explores an info-structural model of cognition for non-interacting agents affected by human sensation, perception, emotion, and affection. We do not analyze the neuroscientific or psychological debate concerning the human mind working, but we underline the importance of modeling the above cognitive levels when designing artificial intelligence agents. Our aim was to start a reflection on the computational reproduction of intelligence, providing a methodological approach through which the aforementioned human factors in autonomous systems are enhanced. The presented model must be intended as part of a larger one, which also includes concepts of attention, awareness, and consciousness. Experiments have been performed by providing visual stimuli to the proposed model, coupling the emotion cognitive level with a supervised learner to produce artificial emotional activity. For this purpose, performances with Random Forest and XGBoost have been compared and, with the latter algorithm, 85% accuracy and 92% coherency over predefined emotional episodes have been achieved. The model has also been tested on emotional episodes that are different from those related to the training phase, and a decrease in accuracy and coherency has been observed. Furthermore, by decreasing the weight related to the emotion cognitive instances, the model reaches the same performances recorded during the evaluation phase. In general, the framework achieves a first emotional generalization responsiveness of 94% and presents an approximately constant relative frequency related to the agent’s displayed emotions. View Full-Text
Keywords: affective computing; sensation; perception; emotion; affection; decision support systems; artificial consciousness affective computing; sensation; perception; emotion; affection; decision support systems; artificial consciousness
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Iovane, G.; Fominska, I.; Landi, R.E.; Terrone, F. Smart Sensing: An Info-Structural Model of Cognition for Non-Interacting Agents. Electronics 2020, 9, 1692.

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