The Novamente AI Engine is briefly reviewed. The overall architecture is unique, drawing on system-theoretic ideas regarding complex mental dynamics and associated emergent patterns. We describe how these are facilitated by a novel knowledge representation which allows diverse cognitive processes to interact effectively. We then elaborate the two primary cognitive algorithms used to construct these processes: probabilistic term logic (PTL), and the Bayesian Optimization Algorithm (BOA). PTL is a highly flexible inference framework, applicable to domains involving uncertain, dynamic data, and autonomous agents in complex environments. BOA is a population-based optimization algorithm which can incorporate prior knowledge. While originally designed to operate on bit strings, our extended version also learns programs and predicates with variable length and treelike structure, used to represent actions, perceptions, and internal state. We detail some of the specific dynamics and structures we expect to emerge through the interaction of the cognitive processes, outline our approach to training the system through experiential interactive learning, and conclude with a description of some recent results obtained with our partial implementation, including practical work in bioinformatics, natural language processing, and knowledge discovery.
The XIA-MAN architecture for intelligent humanoid robot control is proposed, a novel design in which perception and action are achieved via a combination of GAevolved neural-net modules with existing open-source software packages; and cognition is achieved via the OpenCog Prime framework. XML is used to communicate between components, enabling simple pluggability of additional or modified components, and leading to the name XIA-MAN (eXtensible Integrative Artificial Man). XIA-MAN's neural net modules are used to convert between high-dimensional numerical representations denoting perceptions and actions, and probabilistic symbolic representations suitable for cognitive manipulation. XIA-MAN's Cognition Engine is used, at each time cycle, to choose a high-level behavioral procedure that is believes is likely to enable the robot to achieve its goals in the current context. This provides a pragmatic approach to achieving intelligent robot functionality given currently available technologies; and the architecture is conceptually reminiscent of the complexly interconnected multimodular architecture of the brain. Initial work involves an incarnation of the XIA-MAN architecture using the Nao humanoid robot, to create an intelligent humanoid called XiaoNao.
It is argued that any real-world, limited-resources general intelligence is going to manifest a mixture of general principles such as Solomonoff induction and complex self-organizing adaptation, with specific structures and dynamics that reflect corresponding structures and dynamics in the tasks and environments in whose context it was created. This interplay between the general and the specific will play out differently in each type of intelligent system. A number of ideas drawn from previous publications are reviewed here — e.g. cognitive synergy, PGMC and the Mind-World Correspondence Principle — which formalize aspects of this perspective, and provide guidance on how to use it to analyze and create general intelligences.
Goertzel, Ben (Novamente LLC) | Pitt, Joel (Hong Kong Polytechnic University) | Wigmore, Jared (Hong Kong Polytechnic University) | Geisweiller, Nil (Novamente LLC) | Cai, Zhenhua (Xiamen University) | Lian, Ruiting (Xiamen University) | Huang, Deheng (Xiamen University) | Yu, Gino (Hong Kong Polytechnic University)
The hypothesis is presented that "cognitive synergy" -- proactive and mutually-assistive feedback between different cognitive processes associated with different types of memory -- may serve as a foundation for advanced artificial general intelligence. A specific AI architecture founded on this idea, OpenCogPrime, is described, in the context of its application to control virtual agents and robots. The manifestations of cognitive synergy in OpenCogPrime's procedural and declarative learning algorithms are discussed in some detail.
One approach to bridging the historic divide between "symbolic" and "subsymbolic" AI is to incorporate a subsymbolic system and a symbolic system into a synergetic integrative cognitive architecture. Here we consider various issues related to incorporating (subsymbolic) compositional spatiotemporal deep learning networks (CSDLNs, a term introduced to denote the category including HTM, DeSTIN and other similar systems) into an integrative cognitive architecture including symbolic aspects. The core conclusion is that for such integration to be meaningful, it must involve dynamic and adaptive linkage and conversion between CSDLN attractors spanning sensory, motor and goal hierarchies, and analogous representations in the remainder of the integrative architecture. We suggest the mechanism of "semantic CSDLNs", which maintain the general structure of CSDLNs but contain more abstract patterns, similar to those represented in more explicitly symbolic AI systems. This notion is made concrete by describing a planned integration of the DeSTIN CSDLN into the OpenCog integrative cognitive system (which includes a probabilistic-logical symbolic component).