Since the miniaturization of microprocessors, computing power has been embedded in familiar objects such as home appliances and mobile devices; it is gradually pervading almost every level of society. In the last decade, machine learning and pervasive computing technologies have matured to the point where this power is not only integrated with our lives but it can provide context-aware, automated support in our everyday environments. One physical embodiment of such a system is a smart home. In the home environment, computer software that plays the role of an intelligent agent perceives the state of the physical environment and residents using sensors, reasons about this state using artificial intelligence techniques, and then takes actions to achieve specified goals.
During perception, sensors embedded in the home generate readings while residents perform their daily routines. The sensor readings are collected by a computer network and stored in a database that an intelligent agent uses to generate useful knowledge such as patterns, predictions, and trends. On the basis of this information, a smart home can select and automate actions that meet the goals of the smart home application.
The CASAS project treats environments as intelligent agents, where the status of the residents and their physical surroundings are perceived using sensors and the environment is acted upon using controllers in a way that improves the comfort, safety, and/or productivity of the residents. Research groups utilize CASAS datasets for use in their own research, creating a collaborate approach and improving technology evolution.