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, LMCO07a] 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 the current systems in this area are probably not powerful enough to lead to human child level intelligence. We expect that these systems will develop interesting behaviors but fall short of robust preschool level competency, especially in areas like language and reasoning where symbolic systems have typically proved more effective. This intuition is what impels us to pursue a hybrid approach, such as CogPrime. But we do feel that eventually, once the mechanisms underlying brains are better understood and robotic bodies are richer in sensation and more adept in actuation, some sort of emergentist, developmental-robotics approach can be successful at creating humanlike, human-level AGI.
4.4 Hybrid Cognitive Architectures
In response to the complementary strengths and weaknesses of the symbolic and emergentist approaches, in recent years a number of researchers have turned to integrative, hybrid archi- tectures, which combine subsystems operating according to the two different paradigms. The combination may be done in many different ways, e.g. connection of a large symbolic subsystem with a large subsymbolic system, or the creation of a population of small agents each of which is both symbolic and subsymbolic in nature.
Nils Nilsson expressed the motivation for hybrid AGI systems very clearly in his article at the AI-50 conference (which celebrated the 50’th anniversary of the AI field) [Nil09]. While affirming the value of the Physical Symbol System Hypothesis that underlies symbolic AI, he argues that “the PSSH explicitly assumes that, whenever necessary, symbols will be grounded in objects in the environment through the perceptual and effector capabilities of a physical symbol system.” Thus, he continues,
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