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Volume: 2 Issue: 1 Date: March 2010
(Previous issue:Vol. 1, No. 4, December 2009)
Abstract:The interaction of robotics with behavioral and cognitive sciences has always been tight. As often described in the literature, the living has inspired the construction of many robots. Yet, in this article, we focus on the reverse phenomenon: building robots can impact importantly the way we conceptualize behavior and cognition in animals and humans. This article presents a series of paradigmatic examples spanning from the modelling of insect navigation, the experimentation of the role of morphology to control locomotion, the development of foundational representations of the body and of the self/other distinction, the self-organization of language in robot societies, and the use of robots as therapeutic tools for children with developmental disorders. Through these examples, I review the way robots can be used as operational models confronting specific theories to reality, or can be used as proof of concepts, or as conceptual exploration tools generating new hypotheses, or used as experimental set ups to uncover particular behavioral properties in animals or humans, or even used as therapeutic tools. Finally, I discuss the fact that in spite of its role in the formation of many fundamental theories in behavioral and cognitive sciences, the use of robots is far from being accepted as a standard tool and contributions are often forgotten, leading to regular rediscoveries and slowing down cumulative progress. The article concludes by highlighting the high priority of further historical and epistemological work.
Full Text from IEEE:PDF (1151 KB) ; Contact the author by email
Abstract: Real-time search techniques have been used extensively in the areas of task planning and decision making. In order to be effective, however, these techniques require task-specific domain knowledge in the form of heuristic or utility functions. These functions can either be embedded by the programmer, or learned by the system over time. Unfortunately, many of the reinforcement learning techniques that might be used to acquire this knowledge generally demand static feature vector representations defined a priori. Current neurobiological research offers key insights into how the cognitive processing of experience may be used to alleviate dependence on preprogrammed heuristic functions, as well as on static feature representations. Research also suggests that internal appraisals are influenced by such processing and that these appraisals integrate with the cognitive decision-making process, providing a range of useful and adaptive control signals that focus, inform, and mediate deliberation. This paper describes a neuromorphically inspired approach for cognitively processing experience in order to: 1) abstract state information; 2) learn utility functions over this state abstraction; and 3) learn to tradeoff between performance and deliberation time.
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Abstract: How can human infants gradually socialize through interaction with their caregivers? This paper presents a learning mechanism that incrementally acquires social actions by finding and reproducing the contingency in interaction with a caregiver. A contingency measure based on transfer entropy is used to select the appropriate pairs of variables to be associated to acquire social actions from the set of all possible pairs. Joint attention behavior is tested to examine the development of social actions caused by responding to changes in caregiver behavior due to reproducing the found contingency. The results of computer simulations of human–robot interaction indicate that a robot acquires a series of actions related to joint attention such as gaze following and alternation in an order that almost matches the infant development of joint attention found in developmental psychology. The difference in the order between them is discussed based on the analysis of robot behavior, and then future issues are given.
Full Text from IEEE: PDF (1027); Contact the author by email
Abstract: Over the course of development, the central nervous system grows into a complex set of structures that ultimately controls our experiences and interactions with the world. To understand brain development, researchers must disentangle the contributions of genes, neural activity, synaptic plasticity, and intrinsic noise in guiding the growth of axons between brain regions. Here, we examine how computer simulations can shed light on neural development, making headway towards systems that self-organize into fully autonomous models of the brain. We argue that these simulations should focus on the “open-ended” nature of development, rather than a set of deterministic outcomes.
Full Text from IEEE: PDF (665 KB); Contact the author by email