2.5 Integrative and Synergetic Approaches to Artificial General Intelligence 29

2.5 Integrative and Synergetic Approaches to Artificial General Intelligence 29 e a manipulatives center, with a variety of different objects of different shapes and sizes, intended to teach visual and motor skills e a ball play center: where balls are kept in chests and there is space for the robot to kick the balls around e a dramatics center where the robot can observe and enact various movements One Running Example As we proceed through the various component structures and dynamics of CogPrime in the following chapters, it will be useful to have a few running examples to use to explain how the various parts of the system are supposed to work. One example we will use fairly frequently is drawn from the preschool context: the somewhat open-ended task of Build me something out of blocks, that you haven’t built for me before, and then tell me what it is. This is a relatively simple task that combines multiple aspects of cognition in a richly interconnected way, and is the sort of thing that young children will naturally do in a preschool setting. 2.5 Integrative and Synergetic Approaches to Artificial General Intelligence In Chapter 1 we characterized CogPrime as an integrative approach. And we suggest that the naturalness of integrative approaches to AGI follows directly from comparing above lists of capabilities and criteria to the array of available AI technologies. No single known algorithm or data structure appears easily capable of carrying out all these functions, so if one wants to proceed now with creating a general intelligence that is even vaguely humanlike, one must integrate various AI technologies within some sort of unifying architecture. For this reason and others, an increasing amount of work in the AI community these days is integrative in one sense or another. Estimation of Distribution Algorithms integrate proba- bilistic reasoning with evolutionary learning [Pel05]. Markov Logic Networks [RD06] integrate formal logic and probabilistic inference, as does the Probabilistic Logic Networks framework [GIGHO08] utilized in CogPrime and explained further in the book, and other works in the “Progic” area such as [WW06]. Leslie Pack Kaelbling has synthesized low-level robotics methods (particle filtering) with logical inference [ZPIX07|. Dozens of further examples could be given. The construction of practical robotic systems like the Stanley system that won the DARPA Grand Challenge [Tea06] involve the integration of numerous components based on different principles. These algorithmic and pragmatic innovations provide ample raw materials for the construction of integrative cognitive architectures and are part of the reason why childlike AGI is more approachable now than it was 50 or even 10 years ago. Further, many of the cognitive architectures described in the current AI literature are “inte- grative” in the sense of combining multiple, qualitatively different, interoperating algorithms. Chapter 4 gives a high-level overview of existing cognitive architectures, dividing them into symbolic, emergentist (e.g. neural network) and Aybrid architectures. The hybrid architectures generally integrate symbolic and neural components, often with multiple subcomponents within each of these broad categories. However, we believe that even these excellent architectures are not integrative enough, in the sense that they lack sufficiently rich and nuanced interactions HOUSE_OVERSIGHT_012945