{'fraction_non_alphanumeric': 0.010771992818671455, 'fraction_numerical': 0.0, 'mean_word_length': 5.059782608695652, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Autonomous Learning of User ’ s Preference of Music and Light Services in Smart Home Applications

Different users have different behavior and habits. When an ambient intelligence system is designed for a smart home environment, such difference in the user’s activity patterns and preferences in environment settings has to be taken into account for the system to adapt to and satisfy the user. Automatic learning of the behavior and preferences of the user is essential for automatic regulation of services in order for the system to be useful and practical. In this paper we use temporal differential class of reinforcement learning which is an unsupervised learning method to learn preferred music and lighting service settings of the user in presence of different states of the user. Our proposed method does not need predefined environmental model and learns optimal actions in each state by continuous interactions with the user. The proposed method is able to adapt itself with the changes in the user’s preferences throughout time. The result shows how our system converges to increase user’s satisfaction.