In recent years, technological advances have substantially extended the capabilities of building automation. Despite the achieved advances, evidently, automation have not been widely adopted by occupants in buildings. To enhance automation adoptability, automation procedure in buildings should involve determination of user's preferred automation levels, in different conditions and control contexts and learning preference dynamics in time. In line with this motivation, in this paper we introduce a building automation that learns occupant's preferences continuously to fully or partially control the service systems in buildings based on a set of dynamic rules that are generated with the insight about user's preferences and activities. The algorithmic components of our proposed automation include (1) dynamic command planning, (2) adaptive local learning, and (3) iterative global learning. In order to evaluate these algorithms, we used a combination of real and synthetic user activity and preference data from an office with five occupants and an apartment with one occupant. Based on our results from evaluation of adaptive local learning, after a certain number of days (i.e., 8.5 days in average) the accuracy of predicting participants' preference reached to an acceptable value (i.e., above 85%). About 24% to 75%, 5% to 45%, and 6% to 49% of the total daily energy consumption of the participants could be saved using full automation, adaptive automation and inquisitive automation, respectively. Our results for evaluating iterative global learning algorithm showed that adaptive automation has the highest sum of the rewards from achieved benefit and user satisfaction and inquisitive automation has the second highest reward values. Full automation and no automation came in third and last spots, respectively. (C) 2019 Elsevier B.V. All rights reserved.