Virtual environments are often populated by autonomous synthetic agents capable of acting and interacting with other agents as well as with humans. These virtual worlds also include objects that may have different uses and types of interactions. As such, these agents need to identify possible interactions with the objects in the environment and measure the consequences of these interactions. This is particularly difficult when the agents never interacted with some of the objects beforehand. This paper describes SOTAI – Smart ObjecT-Agent Interaction, a framework that will help agents to identify possible interactions with unknown objects based on their past experiences. In SOTAI, agents can learn world regularities, like object attributes and frequent relations between attributes. They gather qualitative symbolic descriptions from their sensorial data when interacting with objects and perform inductive reasoning to acquire concepts about them. We implemented an initial case study and the results show that our agents are able to acquire valid conceptual knowledge.