8.2 Cognitive Synergy 145 —=> ‘CLARATIVE KNOWLEDGE a jer ~ Predict; Maintain : Predictive Implication a nergy My Enersy ) ———— A “hy feu ; Ls : ; Near | Evaluation Link | Ww Fs Hebbian Link ( battery ) eA é : 4. » ¢ / pty & a 4 Hebbian Link = ey get My Energy | Scher ' a a Node Nes Find Entity Near — LJ PROCEDURAL EPISODIC/SENSORY i Pd : KNOWLEDGE KNOWLEDGE —— — ] Fig. 8.1: Illustrative example of the interactions between multiple types of knowledge, in repre- senting a simple piece of knowledge. Generally speaking, one type of knowledge can be converted to another, at the cost of some loss of information. The synergy between cognitive processes associated with corresponding pieces of knowledge, possessing different type, is a critical aspect of general intelligence. synthesis or analysis is just too great, and the algorithms are unable to filter through all the possibilities, given the lack of intrinsic constraint that comes along with a “general intelligence” context (as opposed to a narrow-AI problem like chess-playing, where the context is constrained and hence restricts the scope of possible combinations that needs to be considered). In an AGI architecture based on cognitive synergy, the different learning mechanisms must be designed specifically to interact in such a way as to palliate each others’ combinatorial explosions - so that, for instance, each learning mechanism dealing with a certain sort of knowledge, must synergize with learning mechanisms dealing with the other sorts of knowledge, in a way that decreases the severity of combinatorial explosion. One prerequisite for cognitive synergy to work is that each learning mechanism must rec- ognize when it is “stuck,” meaning it’s in a situation where it has inadequate information to make a confident judgment about what steps to take next. Then, when it does recognize that it’s stuck, it may request help from other, complementary cognitive mechanisms. A theoretical notion closely related to cognitive syn