The University of Edinburgh -
Division of Informatics
Forrest Hill & 80 South Bridge

MSc Thesis #9763

Title:Artificialontogenesis: Cognitive and Behavioural Development for Robots
Date: 1997
Abstract:There are three classes of adaptive process (structural definition, structural adjustment, and parameter adjustment) which appear to underly the development of intelligence in nature. In artificial intelligence only two of these processes are used; AI ignores development (structural adjustment). While AI attempts to predefine explicit rules for behaviour, nature's success in building complex creatures depends on predefining how rules to control behaviour can be learned. It is the developmental processes in biology through which such rules are learned. This proposal is to apply mechanisms similar to those used in biological development to robots. this will move robotics from "development" meaning design and production, towards "development" in its biological sense meaning a process of growth and progressive change. Defining the rules for development is design at a meta-level to that currently used. It is proposed that the long process of evolution used by nature to define these developmental processes might be supplanted by another adaptive process, that of engineering, to more quickly enable study of ontogenetic development. This project thus aims to apply techniques inspired by animal development to engineering robot control systems. Specifically it is proposed that a hierarchical control system, based on the cerebral cortex, is used and that this develops through constructivist learning algorithms (ones in which the interaction of a situated agent with its environment guides the creation of cognitive machinery appropriate for representing and acting in that environment). Such a robot would be provided with some innate, low-level, behavioural abilities and through experience develop more complex behaviour.

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