AbstractThis paper is dedicated to the study of existing approaches that explicitly use mental simulation. Current implementations of the mental simulation paradigm, taken together, computationally address many aspects suggested by cognitive science research. Agents are able to find solutions to nontrivial scenarios in virtual or physical environments. Existing systems also learn new behavior by imitation of others similar to them and model the behavior of different others with the help of specialized models, culminating with the collaboration between agents and humans. Approaches that use self models are able to mentally simulate interaction and to learn about their own physical properties. Multiple mental simulations are used to find solutions to tasks, for truth maintenance, and contradiction detection. However, individual approaches do not cover all of the contexts of mental simulation and most rely on techniques which are only suitable for subsets of obtainable functionality. This review spans through four perspectives on the functionality of state‐of‐the‐art artificial intelligence applications, while linking them to cognitive science research results. Finally, an overview identifies the main gaps in existing literature on computational mental simulation and provides our suggestions for future development.