Development imposes great challenges. Internal “cortical” representations must be autonomously generated from interactive experiences. The eventual quality of these developed representations is of course important. Additionally, learning must be as fast as possible—to quickly derive better representation from limited experiences. Those who achieve both of these will have competitive advantages. We present a cortex-inspired theory called lobe component analysis (LCA) guided by the aforementioned dual criteria. A lobe component represents a high concentration of probability density of the neuronal input space. We explain how lobe components can achieve a dual—spatiotemporal (“best ” and “fastest”)—optimality, through mathematical analysis, in which we describe how lobe components ’ plasticity can be temporally scheduled to take into account the history of observations in the best possible way. This contrasts with using only the last observation in gradient-based adaptive learning algorithms. Since they are based on two cell-centered mechanisms—Hebbian learning and lateral inhibition—lobe components develop in-place, meaning every networked neuron is individually responsible for the learning of its signal-processing characteristics within its connected network environment. There is no need for a separate learning network. We argue that in-place learning algorithms will be crucial for real-world large-size developmental applications due to their simplicity, low computational complexity, and generality. Our experimental results show that the learning speed of the LCA algorithm is drastically faster than other Hebbian-based updating methods and independent component analysis algorithms, thanks to its dual optimality, and it does not need to use any second- or higher order statistics. We also introduce the new principle of fast learning from stable representation.

Artificial neural networks can model cortical local learning and signal processing, but they are not the brain, neither are many special purpose systems to which they contribute. Autonomous mental development models all or part of the brain (or the central nervous system) and how it develops and learns autonomously from infancy to adulthood. Like neural network research, such modeling aims to be biologically plausible. This paper discusses why autonomous development is necessary according to a concept called task muddiness. Then it introduces recent results for a series of research issues, including the new paradigm for autonomous development, mental architectures, developmental algorithm, a refined classification of types of machine learning, spatial complexity and time complexity. Finally, the paper presents some experimental results for applications, including: visionguided navigation, object finding, object-based attention (eye-pan), and attention-guided pre-reaching, four tasks which infants learn to perform early but very perceptually challenging for robots. Key words: cognitive development, autonomous learning, mental architecture, on-line learning, incremental learning, visual learning, working memory, long-term memory, self-organization, regression, autonomous navigation, attention selection, object recognition,

Abstract—Scaffolding is a process of transferring learned skills to new and more complex tasks through arranged experience in open-ended development. In this paper, we propose a developmental learning architecture that enables a robot to transfer skills acquired in early learning settings to later more complex task settings. We show that a basic mechanism that enables this transfer is sequential priming combined with attention, which is also the driving mechanism for classical conditioning, secondary conditioning, and instrumental conditioning in animal learning. A major challenge of this work is that training and testing must be conducted in the same program operational mode through online, real-time interactions between the agent and the trainers. In contrast with former modeling studies, the proposed architecture does not require the programmer to know the tasks to be learned and the environment is uncontrolled. All possible perceptions and actions, including the actual number of classes, are not available until the programming is finished and the robot starts to learn in the real world. Thus, a predesigned task-specific symbolic representation is not suited for such an open-ended developmental process. Experimental results on a robot are reported in which the trainer shaped the behaviors of the agent interactively, continuously, and incrementally through verbal commands and other sensory signals so that the robot learns new and more complex sensorimotor tasks by transferring sensorimotor skills learned in earlier periods of open-ended development. Index Terms—Attention, classical conditioning, incremental learning, instrumental conditioning, mental architecture, mental development, multitask learning, online learning, scaffolding, skill transfer. I.

This paper presents a computational theory of developmental mental architectures for artificial and natural systems, motivated by neuroscience. The work is an attempt to approximately model biological mental architectures using mathematical tools. Six types of architecture are presented, beginning with the observation-driven Markov decision process as Type-1. From Type-1 to Type-6, the architecture progressively becomes more complete toward the necessary functions of autonomous mental development. Properties of each type are presented. Experiments are discussed with emphasis on their architectures. r 2007 Published by Elsevier B.V.

Abstract—There exists a large conceptual gap between symbolic models and emergent models for the mind. Many emergent models work on low-level sensory data, while many symbolic models deal with high-level abstract (i.e., action) symbols. There has been relatively little study on intermediate representations, mainly because of a lack of knowledge about how representations fully autonomously emerge inside the closed brain skull, using information from the exposed two ends (the sensory end and the motor end). As reviewed here, this situation is changing. A fundamental challenge for emergent modelsisabstraction,which symbolic models enjoy through human handcrafting. The term abstract refers to properties disassociated with any particular form. Emergent abstraction seems possible, although the brain appears to never receive a computer symbol (e.g., ASCII code) or produce such a symbol. This paper reviews major agent models with an emphasis on representation. It suggests two different ways to relate symbolic representations with emergent representations: One is based on their categorical definitions. The other considers that a symbolic representation corresponds to a brain’s outside behaviors observed and handcrafted by other outside human observers; but an emergent representation is inside the brain. Index Terms—Agents, attention, brain architecture, complexity, computer vision, emergent representation, graphic models, mental