Neural Networks for Coordination and Control: The Portability of Experiential Representations

It is time to locate Connectionist representation theory in the new wave of robotics research. The utility of representations developed in Artificial Neural Networks during learning has been demonstrated in Cognitive Science research since the 1980s. The research reported here puts learned representations to work in a decentered control task, the disembodied arm problem, in which a mobile robot operates an arm fixed to a table to pick up objects. There is no physical linkage between the arm and the robot and so the robot's point of view must be decentered. This is done by developing a modular Artificial Neural Net system in three stages: (i) a Classifier net is trained with laser scan data; (ii) an Arm net is trained for picking up objects; (iii) an Inter net is trained to communicate and coordinate the sensing and acting. The completed system is shown to create new nonsymbolic transformationally invariant representations in order to perform the effective generalisation of decentered v..

Learning from innate behaviors: A Quantitative Evaluation of Neural Network Controllers

this paper, only the front seven sonar and seven IR sensors were used. These covered approximately 15

Connectionism And The Issues Of Compositionality And Systematicity

Connectionism as a model of the mind has been attacked by the advocators of the classical paradigm, who claim that Connectionism can only work if it is an implementation of Classical representations. This could be true for some of the models that claim to be Connectionist, but it will in this paper be shown that this is not true for Connectionist architectures that use non-symbolic representations. We will provide evidence in the form of simulation results that severely weaken of the arguments raised by Fodor and Pylyshyn and Fodor and McLaughlin, including their two main arguments, which are the lack of compositionality and systematicity. 1 INTRODUCTION It has been argued that Connectionist models of the mind are mere implementations of Classical models, which are characterised, according to Fodor and Pylyshyn [1], by, 1) Combinatorial syntax and semantics for mental representations... in which (a) there is a distinction between structurally atomic and structurally molecular..

A Consideration of the Biological and Psychological Foundations of Autonomous Robotics

The new wave of robotics aims to provide robots with the capacity to learn, develop and evolve in interaction with their environments using biologically inspired techniques. This work is placed in perspective by considering its biological and psychological basis with reference to some of the grand theorists of living systems. In particular, we examine what it means to have a body by outlining theories of the mechanisms of bodily integration in multicellular organisms and their means of solidarity with the environment. We consider the implications of not having a living body for current ideas on robot learning, evolution, and cognition and issue words of caution about wishful attributions that can smuggle more into observations of robot behaviour than is scientifically supportable. To round off the arguments we take an obligatory swipe at ungrounded artificial intelligence but quickly move on to assess physical grounding and embodiment in terms of the rooted cognition of the living

Connectionism - The Miracle Mind Model

Connectionism as a model of the mind has recently been challenging the Classical model, in which the mind is regarded as symbol manipulating system. The main arguments against Connectionism concern its inability to form mental representations for complex expressions, which can be used for structure sensitive operations. Some argue for hybrid models which combine some of the most attractive features of the Classical and Connectionist models. This paper starts off by examining the definitions of the different approaches and also their strengths and weaknesses. One section is devoted to the debate between the advocators of the different paradigms, including the arguments about the lack of compositionality and systematicity in Connectionist cognitive models. We then argue for the Connectionist approach as the most attractive model of the mind. This includes performing the "miracle" of defining structure sensitive operations on non-symbolic representations of concepts. 1 INTRODUCTION Ther..