Systems for Ambient Intelligence contexts are expected to exhibit an autonomous and intelligent behavior, by understanding and reacting to the activities that take place in such contexts. These activities, specially those labelled as trivial or simple tasks, are carried out in an eﬀortless manner by most of the people. In contrast to what it might be expected, computers are struggled to deal with these activities, while easily performing some others, such as high proﬁle calculations, so hard for humans. Imagine a situation where, while holding an object, the holder walks to a contiguous room. We have eﬀortless inferred that the object is changing its location along with its holder. However, these sort of inferences are not well addressed by computers, due to their lack of common-sense knowledge and reasoning capability. Providing systems with these two issues implies collecting a great deal of knowledge about the everyday life, and implementing inference mechanisms to derive new information from it. The work proposed here advocates for a common- sense approach as a solution to the shortages of current systems for Ambient Intelligence.