4 Operations on these representations include reinforcement learning and classification, perceptual top-down/bottom-up processing, abstraction, analogy formation, associative and syllogistic reasoning, planning, reflection, anticipation and several modes of reorganization. Creating a scientific community The task of the Structure of Mental Representation initiative will at first consist in the creation of a large and competitive community, built around a set of benchmark problems. I suggest picking a set of problems that covers most of the above architectural requirements: the comprehension of dynamic visual sequences (movies) and narratives. The evaluation of comprehension may focus both on a discourse level (asking the system general and specific questions about the consumed visuals or narratives) and directly, by producing dynamic renderings and depictions of knowledge represented within the system. At least on the discourse level, a direct comparison to the functional properties of child performance at different cognitive stages is possible. The Com prehension Turning the benchmark task into a regular competition allows a direct comparison between models, and offers a strong Challenge ee . incentive for exchange of solutions among research groups. The need to for broad solutions with given material and intellectual resources will enforce a higher degree of the reuse of code and ideas than we currently see in AJ architectures (outside of robotic soccer, where such competitions have turned out to be highly successful). By structuring the comprehension tasks into different levels and sub-domains (such as basic language acquisition tasks, grammar, perceptual and logical tasks, social reasoning/theory of mind, affective evaluation, constructive problem solving, use of analogies and metaphor etc.), we can formulate consistent long-term goals and realistic short-term problems. To pull the Structure of Mental Representation initiative off the ground, we will need to cre