Abstract. An autonomous agent may largely benefit from its ability to reconstruct another agent’s reasoning principles from records of past events and general knowledge about the world. In our approach, the meta-agent maintains a first-order logic theory, called the community model, yielding predictions about other agents’ decisions. In this contribution we introduce a query-based collective reasoning process where the semi-collaborative meta-agents use active learning technique to improve their models. We provide empirical results that demonstrate the viability of the concept and show the benefits of collective meta-reasoning.