A Multi-Agent Model for Educational consciousness Agent
Lourdes Sanchez-Guerrero, Universidad Autonoma Metropolitana-Unidad Azcapotzalco, Mexico ; Ana Lilia Laureano-Cruces, Martha Mora-Torres, Javier Ramírez-Rodríguez, Universidad Autónoma Metropolitana-Azcapotzalco, Mexico
E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, in Washington, DC, United States Publisher: Association for the Advancement of Computing in Education (AACE), San Diego, CA
A common question is how can a teacher act or feel when he is immersed in the teaching-learning process? There is a whole chained reasoning based on the experience of the teacher. A pedagogical agent is the interface that enables communication between an intelligent teaching system and the user. This paper presents a model of this process of reasoning which we will call consciousness in order to include it in the decision-making process. This implies the development of an inference machine that takes into consideration what the teacher feels, including what are the feelings about the user behavior. This means considering emotions as detonate for the latest action. This paper reviews the concept of consciousness from engineering point of view. The above, in order to design an artificial consciousness computer model; which can be implemented as an inference engine. This will improve the decision-making process of the pedagogical agent.
Sanchez-Guerrero, L., Laureano-Cruces, A.L., Mora-Torres, M. & Ramírez-Rodríguez, J. (2016). A Multi-Agent Model for Educational consciousness Agent. In Proceedings of E-Learn: World Conference on E-Learning (pp. 471-480). Washington, DC, United States: Association for the Advancement of Computing in Education (AACE).
© 2016 Association for the Advancement of Computing in Education (AACE)
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Lourdes Sanchez-Guerrero, Ana Lilia Laureano-Cruces, Perla Velasco-Santos, Martha Mora-Torres & Javier Ramirez-Rodriguez, Universidad Autonoma Metropolitana-Azcapotzalco, Mexico
E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2017 (Oct 17, 2017) pp. 449–458
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