4.4 Hybrid Cognitive Architectures 73 straction. IM-CLEVER’s motivational structure is based in part on Schmidhuber’s information- theoretic model of curiosity [Sch06]; and CogPrime’s Psi-based motivational structure utilizes probabilistic measures of novelty, which are mathematically related to Schmidhuber’s mea- sures. On the other hand, IM-CLEVER’s use of reinforcement learning follows Schmidhuber’s earlier work RL for cognitive robotics [BS04, BZGS06], Barto’s work on intrinsically motivated reinforcement learning [SB06, SM05], and Lee’s [LMC07b, LMC07a] work on developmental reinforcement learning; whereas CogPrime’s assemblage of learning algorithms is more diverse, including probabilistic logic, concept blending and other symbolic methods (in the OCP compo- nent) as well as more conventional reinforcement learning methods (in the DeSTIN component). In many respects IM-CLEVER bears a moderately strong resemblance to DeSTIN, whose integration with CogPrime is discussed in Chapter 26 of Part 2 (although IM-CLEVER has much more focus on biological realism than DeSTIN). Apart from numerous technical differ- ences, the really big distinction between IM-CLEVER and CogPrime is that in the latter we are proposing to hybridize a hierarchical-abstraction/reinforcement-learning system (such as DeSTIN) with a more abstract symbolic cognition engine that explicitly handles probabilistic logic and language. IM-CLEVER lacks the aspect of hybridization with a symbolic system, tak- ing more of a pure emergentist strategy. Like DeSTIN considered as a standalone architecture IM-CLEVER does entail a high degree of cognitive synergy, between components dealing with perception, world-modeling, action and motivation. However, the “emergentist versus hybrid” is a large qualitative difference between the two approaches. In all, while we largely agree with the philosophy underlying developmental robotics, our intuition is that the learning and representational mechanisms underlying t