144 8 Cognitive Synergy regarding the measurement of the simplicity of goals and environments; but the points made here do not rely on that argument. What they do rely on is the assumption that, in the intelligence in question, the different components of memory are significantly but not wholly distinct. That is, there are significant “family resemblances” between the memories of a single type, yet there are also thoroughgoing connections between memories of different types. The cognitive synergy principle, if correct, applies to any AI system demonstrating intelli- gence in the context of embodied, social communication. However, one may also take the theory as an explicit guide for constructing AGI systems; and of course, the bulk of this book describes one AGI architecture, CogPrime, designed in such a way. It is possible to cast these notions in mathematical form, and we make some efforts in this direction in Appendix ??, using the languages of category theory and information geometry. However, this formalization has not yet led to any rigorous proof of the generality of cognitive synergy nor any other exciting theorems; with luck this will come as the mathematics is further developed. In this chapter the presentation is kept on the heuristic level, which is all that is critically needed for motivating the CogPrime design. 8.2 Cognitive Synergy The essential idea of cognitive synergy, in the context of multi-memory systems, may be ex- pressed in terms of the following points: 1. Intelligence, relative to a certain set of environments, may be understood as the capability to achieve complex goals in these environments. 2. With respect to certain classes of goals and environments (see Chapter 9 for a hypothe- sis in this regard), an intelligent system requires a “multi-memory” architecture, meaning the possession of a number of specialized yet interconnected knowledge types, including: declarative, procedural, attentional, sensory, episodic and intentional (goa