The technologies used in smart homes have recently improved to learn the user preferences from feedback in order to enhance the user convenience and quality of experience. Most smart homes learn a uniform model to represent the thermal preferences of users, which generally fails when the pool of occupants includes people with different sensitivities to temperature, for instance due to age and physiological factors. Thus, a smart home with a single optimal policy may fail to provide comfort when a new user with a different preference is integrated into the home. In this paper, we propose a Bayesian Reinforcement learning framework that can approximate the current occupant state in a partially observable smart home environment using its thermal preference, and then identify the occupant as a new user or someone is already known to the system. Our proposed framework can be used to identify users based on the temperature and humidity preferences of the occupant when performing different activities to enable personalization and improve comfort. We then compare the proposed framework with a baseline long short-term memory learner that learns the thermal preference of the user from the sequence of actions which it takes. We perform these experiments with up to 5 simulated human models each based on hierarchical reinforcement learning. The results show that our framework can approximate the belief state of the current user just by its temperature and humidity preferences across different activities with a high degree of accuracy.

The road to the autonomous driving future is certainly not going to be easy. Some car makers are even starting to doubt we'll ever get completely self-driving cars. Level 5 autonomous driving models will basically no longer need a steering wheel. They will be capable of taking us everywhere without any kind of help from the human occupants. But until we get there, Level 4 cars might be more plausible.

Wait, change that, go ahead and panic. Sometimes people momentarily lose their minds and opt to panic. This primal urge can be handy as it invokes the classic fight-or-flight instinctive reaction to a situation. If you suddenly see a bear up ahead while in the woods, it could be that rather than carefully trying to plot out all of the myriad of options about what to do, entering instead into a panic mode might get your feet moving and you'll have run far from the bear before it has had a chance to do anything to you. On the other hand, it could be that your effort to run away is not wise and the bear easily catches up with you, allowing the bear to win and perhaps an untoward result for you. Not many of us will likely get into a circumstance of confronting a bear, and so let's consider something that might be higher odds of happening to any of us. Suppose you are in an airplane and the plane is on the ground and engaged in fire. Presumably, with or without panic, you'd realize that you should get out of the burning airplane. How can you get out of the burning airplane? I'm sure you've all sat through the flight attendants telling you to figure out beforehand the nearest exit to your seat. I'd bet that most people don't look to see where that exit is, and instead just kind of assume that when there's an emergency they'll figure out where the exit is.