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USER-BASED LEARNING MODEL FOCUSED ON INTELLIGENT HOME

Alejandro Humberto Garcia Ruiz, Salvador Ibarra Martínez, José Antonio Castán Rocha, Julio Laria Menchaca, Jesús David Terán Villanueva

Abstract


Intelligent environments are becoming more integrated into everyday life, making intelligent homes, reality. In this sense, this paper presents the results of a user-based learning model, dedicated to predict the human preferences in an intelligent home. The objective was to assess and determine the average performance of the proposed model for diverse types of users with different preferences each. Our model allows us to endow an intelligent home with the ability to learn from users, for the subsequent prediction of their preferences on the use of household appliances. In specific, the proposed model solves the adequate control of home lights. A methodology based on the artificial intelligence (AI) technique known as case-based reasoning (CBR) was used for the development of this model. Likewise, the Inductive Decision Tree (ID3) technique was implemented in the decision making of the model to provide with a suitable and trustworthy option for the studied problem. Two cases of study were evaluated with four diverse types of users each to assess the learning model. ID3 was able to predict all type of user preferences with an average performance of 98% in a set of 10,000 experiments. Additionally, the model achieved stability after 3,500 experiments. The stabilization capacity of the model was tested with the application of the cases of study, concluding that the model is able to provide solutions to the preferences of users belonging to completely different lifestyles. Finally, some conclusions are presented, emphasizing the advantages of using ID3 in the intelligent home decision making.


Keywords


Case-Based Reasoning; Inductive Decision Tree; Intelligent Home; Intelligent Environments; Ubiquitous Computing

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References


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