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 Then I argue that early formulations of these reasons are outdated.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

Stable Aspects in Robot Software Development

The paper investigates the concept of software “stability” applied to robot systems. We define “stable” a family of systems modelled, designed and implemented so that specific applications of the family may be developed re-using, adapting and specializing knowledge, architecture and existing components. During the last few years, many ideas and technologies of software engineering (e.g. modularity, OO development and design patterns) were introduced in the development of robotic systems to improve the “stability” property. All these ideas and technologies are important. Nevertheless, they model robotic systems along a unique direction: the functional decomposition of parts. Unfortunately, there are concerns of robotic systems that relate to the systems as a whole hence crosscutting their modular structure. The Aspect Oriented Software Development is a recently emerged approach for modelling, designing and encapsulating the above-mentioned crosscutting concerns (aspects). We contend that stability must be based on a careful domain analysis and on a multidimensional modelling of different and recurring aspects of robot systems

Synthetic Phenomenology

The term "synthetic phenomenology" refers to: 1) any attempt to characterize the phenomenal states possessed, or modeled by, an artefact (such as a robot); or 2) any attempt to use an artefact to help specify phenomenal states (independently of whether such states are possessed by a naturally conscious being or an artefact). The notion of synthetic phenomenology is clarified, and distinguished from some related notions. It is argued that much work in machine consciousness would benefit from being more cognizant of the need for synthetic phenomenology of the first type, and of the possible forms it may take. It is then argued that synthetic phenomenology of the second type looks set to resolve some problems confronted by standard, non-synthetic attempts at characterizing phenomenal states. An example of the second form of synthetic phenomenology is given

Brain inspired cognitive systems (BICS)

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