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Fittner, M., & Brandstätter, C. (2016). Emotional Learning in a Simulated Model of the Mental Apparatus. In Computer Science & Information Technology (pp. 1–7). Computer Science Conference Proceedings. http://hdl.handle.net/20.500.12708/75595
Second International Conference on Computer Science, Information Technology and Applications
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Event date:
30-Dec-2016 - 31-Dec-2016
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Event place:
Zurich, Non-EU
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Number of Pages:
7
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Publisher:
Computer Science Conference Proceedings, 7 / 1
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Peer reviewed:
Yes
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Keywords:
Emotion; Learning; Artificial General Intelligence; Cognitive Architectures; Simulation of Mental Apparatus and Applications (SiMA); Artificial Recognition System (ARS); Cognitive Automation; Psychoanalytically Inspired AI; Software Agents
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Abstract:
How a human being learns is a wide field and not fully understood until now. This paper should give an alternative attempt to get closer to the answer how human beings learn something and what the relation to emotions is. Therefore, the cognitive architecture of the project Simulation of Mental Apparatus and Applications (SiMA) is used to fulfill two tasks. One is to give an answer to the question...
How a human being learns is a wide field and not fully understood until now. This paper should give an alternative attempt to get closer to the answer how human beings learn something and what the relation to emotions is. Therefore, the cognitive architecture of the project Simulation of Mental Apparatus and Applications (SiMA) is used to fulfill two tasks. One is to give an answer to the question above and the other on is to enhance the functional model of the mental apparatus with learning. For that reason, the functions of the model are analyzed in detail for their ability to enhance them with a learning ability. The focus of the analysis lay on emotions and their impact on the ability to change memories in the model to determine a different behavior than without learning.
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Research Areas:
Sensor Systems: 30% Automation and Robotics: 35% Computer Engineering and Software-Intensive Systems: 35%
