Currently, there is a lack of general-purpose, in-place learning engines that incrementally learn multiple tasks, to develop “soft ” multi-task-shared invariances in the intermediate internal representation while a developmental robot interacts with its environment. In-place learning is a biologically inspired concept, rooted in the genomic equivalence principle, meaning that each neuron is responsible for its own development while interacting with its environment. With in-place learning, there is no need for a separate learning network. Computationally, biologically inspired, in-place learning provides unusually efficient learning algorithms whose simplicity, low computational complexity, and generality are set apart from typical conventional learning algorithms. We present in this paper the multiple-layer in-place learning network (MILN) for this ambitious goal. As a key requirement for autonomous mental development, the network enables both unsupervised and supervised learning to occur concurrently, depending on whether motor supervision signals are available or not at the motor end (the last layer) during the agent’s interactions with the environment. We present principles based on which MILN automatically develops invariant neurons in different layers and why such invariant neuronal clusters

Attention focusing mechanisms and domaininformed selection of representations can make realtime vision tasks work with limited computational power. This paper describes ongoing work in distributed real-time vision which aims to use cheap and plentiful workstations and PCs rather than specialpurpose hardware. I discuss a system called Argus which is inspired by the visual routines theory of human vision. In Argus, reactive feature tracking agents maintain minimal, task-dependent descriptions of relevant image features by direct observation of the live video stream. Routines for model-based object recognition operate on these descriptions. Higherlevel processing is independent of the maintenance of lower-level representations. This allows the visual subsystem to provide real-time feedback for closed-loop tasks even when high-level perceptual processing is slow compared to video frame rates. Experiments in moving-object recognition are described which demonstrate the strength of this app...

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.

This article introduces the developmental approach to artificial intelligence, which is different from other existing major approaches: knowledge-based, behavior-based, learning-based, and evolutionary approaches. The developmental approach is motivated by human cognitive development from infancy to adulthood, during which human individuals develop their intelligence through interactions with the environment. A developmental algorithm of a species, either natural or artificial, starts to run at the "birth" of the individual and it runs continuously through the entire life span. It automates the process of system development. The developmental approach does not mean just from small to big and from simple to complex. It requires the system to learn new tasks and new aspects of each complex task without a need of reprogramming. We introduce AA-learning as a basic mode for developmental learning. This paper introduces the basic concepts, the architecture, some developmental algorithms, and...

. A model for attentional scanning is constructed in the form of a gating network which consists of gating lattices. A gating lattice is a sparsely-connected neural network. The process of covert attention is interpreted as a biological solution to the problem of translation-invariant pattern processing. We arrive at the final result by a sequence of pattern translations channelled through the gating network. Simulation studies and theoretical considerations reveal that the gating lattice gives rise to a trade off between gating quality and gating flexibility. The gating network is shown to be capable of translation-invariant processing of object patterns that are part of a natural image. 1 BACKGROUND Visual systems succeed remarkably well in extracting invariant properties of incoming patterns. An example of such a property is object identity. Visual priming (i.e., the facilitated speed and enhanced accuracy of identification due to prior object exposure) has been shown to be indepen...