270 13 Local, Global and Glocal Knowledge Representation The activation of the key will generally cause the activation of the map, and the activation of a significant percentage of the map will cause the activation of the rest of the map, including the key. Furthermore, if the key were for some reason forgotten, then after a significant amount of effort, the system would likely to be able to reconstitute it (perhaps with various small changes) from the information in the map. We conjecture that this particular kind of glocal memory will turn out to be very powerful for AGI, due to its ability to combine the strengths of formal logical inference with those of self-organizing attractor neural networks. As a simple example, consider the representation of a “tower”, in the context of an artificial agent that has built towers of blocks, and seen pictures of many other kinds of towers, and seen some tall building that it knows are somewhat like towers but perhaps not exactly towers. If this agent is reasonably conceptually advanced (say, at Piagetan the concrete operational level) then its mind will contain some declarative relationships partially characterizing the concept of “tower,” as well as its sensory and episodic examples, and its procedural knowledge about how to build towers. The key of the “tower” concept in the agent’s mind may consist of internal images and episodes regarding the towers it knows best, the essential operations it knows are useful for building towers (piling blocks atop blocks atop blocks...), and the core declarative relations summarizing “towerness” — and the whole “tower” map then consists of a much larger number of images, episodes, procedures and declarative relationships connected to “tower” and other related entities. If any portion of the map is removed — even if the key is removed — then the rest of the map can be approximately reconstituted, after some work. Some cognitive operations are best done on the localized representation —