25 research outputs found
Some Experiments on the influence of Problem Hardness in Morphological Development based Learning of Neural Controllers
Natural beings undergo a morphological development process of their bodies
while they are learning and adapting to the environments they face from infancy
to adulthood. In fact, this is the period where the most important learning
pro-cesses, those that will support learning as adults, will take place.
However, in artificial systems, this interaction between morphological
development and learning, and its possible advantages, have seldom been
considered. In this line, this paper seeks to provide some insights into how
morphological development can be harnessed in order to facilitate learning in
em-bodied systems facing tasks or domains that are hard to learn. In
particular, here we will concentrate on whether morphological development can
really provide any advantage when learning complex tasks and whether its
relevance towards learning in-creases as tasks become harder. To this end, we
present the results of some initial experiments on the application of
morpho-logical development to learning to walk in three cases, that of a
quadruped, a hexapod and that of an octopod. These results seem to confirm that
as task learning difficulty increases the application of morphological
development to learning becomes more advantageous.Comment: 10 pages, 4 figure
From Computer Metaphor to Computational Modeling: The Evolution of Computationalism
In this paper, I argue that computationalism is a progressive research tradition. Its metaphysical assumptions are that nervous systems are computational, and that information processing is necessary for cognition to occur. First, the primary reasons why information processing should explain cognition are reviewed. Then I argue that early formulations of these reasons are outdated. However, by relying on the mechanistic account of physical computation, they can be recast in a compelling way. Next, I contrast two computational models of working memory to show how modeling has progressed over the years. The methodological assumptions of new modeling work are best understood in the mechanistic framework, which is evidenced by the way in which models are empirically validated. Moreover, the methodological and theoretical progress in computational neuroscience vindicates the new mechanistic approach to explanation, which, at the same time, justifies the best practices of computational modeling. Overall, computational modeling is deservedly successful in cognitive (neuro)science. Its successes are related to deep conceptual connections between cognition and computation. Computationalism is not only here to stay, it becomes stronger every year
Perceptual Social Dimensions of Human-Humanoid Robot Interaction
The present paper aims at a descriptive analysis of the main perceptual and social features of natural conditions of agent interaction, which
can be specified by agent in human-humanoid robot interaction. A principled
approach to human-robot interaction may be assumed to comply with the
natural conditions of agents overt perceptual and social behaviour. To validate our research we used the minimalistic humanoid robot Telenoid. We have
conducted human-robot interactions test with people with no prior interaction experience with robot. By administrating our questionnaire to subject
after well defined experimental conditions, an analysis of significant variance
correlation among dimensions in ordinary and goal guided contexts of interaction has been performed in order to prove that perception and believability
are indicators of social interaction and increase the degree of interaction in
human-humanoid interaction. The experimental results showed that Telenoid
is seen from the users as an autonomous agent on its own rather than a teleoperated artificial agent and as a believable agent for its naturally acting in
response to human agent actions
Modelling consciousness-dependent expertise in machine medical moral agents
It is suggested that some limitations of current designs for medical AI systems (be they autonomous or advisory) stem from the failure of those designs to address issues of artificial (or machine) consciousness. Consciousness would appear to play a key role in the expertise, particularly the moral expertise, of human medical agents, including, for example, autonomous weighting of options in (e.g.,) diagnosis; planning treatment; use of imaginative creativity to generate courses of action; sensorimotor flexibility and sensitivity; empathetic and morally appropriate responsiveness; and so on. Thus, it is argued, a plausible design constraint for a successful ethical machine medical or care agent is for it to at least model, if not reproduce, relevant aspects of consciousness and associated abilities. In order to provide theoretical grounding for such an enterprise we examine some key philosophical issues that concern the machine modelling of consciousness and ethics, and we show how questions relating to the first research goal are relevant to medical machine ethics. We believe that this will overcome a blanket skepticism concerning the relevance of understanding consciousness, to the design and construction of artificial ethical agents for medical or care contexts. It is thus argued that it would be prudent for designers of MME agents to reflect on issues to do with consciousness and medical (moral) expertise; to become more aware of relevant research in the field of machine consciousness; and to incorporate insights gained from these efforts into their designs