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Volume: 1 Issue: 3 Date: October 2009
(Previous issue:Vol. 1, No. 2, August 2009)
Abstract: Intelligent adaptive curiosity (IAC) was initially introduced as a developmental mechanism allowing a robot to self-organize developmental trajectories of increasing complexity without preprogramming the particular developmental stages. In this paper, we argue that IAC and other intrinsically motivated learning heuristics could be viewed as active learning algorithms that are particularly suited for learning forward models in unprepared sensorimotor spaces with large unlearnable subspaces. Then, we introduce a novel formulation of IAC, called robust intelligent adaptive curiosity (R-IAC), and show that its performances as an intrinsically motivated active learning algorithm are far superior to IAC in a complex sensorimotor space where only a small subspace is neither unlearnable nor trivial. We also show results in which the learnt forward model is reused in a control scheme. Finally, an open source accompanying software containing these algorithms as well as tools to reproduce all the experiments presented in this paper is made publicly available.
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Abstract: In tasks such as pursuit and evasion, multiple agents need to coordinate their behavior to achieve a common goal. An interesting question is, how can such behavior be best evolved? A powerful approach is to control the agents with neural networks, coevolve them in separate subpopulations, and test them together in the common task. In this paper, such a method, called Multiagent Enforced SubPopulations (Multiagent ESP), is proposed and demonstrated in a prey-capture task. First, the approach is shown to be more efficient than evolving a single central controller for all agents. Second, cooperation is found to be most efficient through stigmergy, i.e., through role-based responses to the environment, rather than communication between the agents. Together these results suggest that role-based cooperation is an effective strategy in certain multiagent tasks.
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Abstract: A robot that can communicate with humans using natural language will have to acquire a grammatical framework. This paper analyses some crucial underlying mechanisms that are needed in the construction of such a framework. The work is inspired by language acquisition in infants, but it also draws on the emergence of language in evolutionary time and in ontogenic (developmental) time. It focuses on issues arising from the use of real language with all its evolutionary baggage, in contrast to an artificial communication system, and describes approaches to addressing these issues. We can deconstruct grammar to derive underlying primitive mechanisms, including serial processing, segmentation, categorization, compositionality, and forward planning. Implementing these mechanisms are necessary preparatory steps to reconstruct a working syntactic/semantic/pragmatic processor which can handle real language. An overview is given of our own initial experiments in which a robot acquires some basic linguistic capacity via interacting with a human.
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Abstract: Attachment, or the emotional tie between an infant and its primary caregiver, has been modeled as a homeostatic process by Bowlby's (Attachment and Loss, 1969; Anxiety and Depression, 1973; Loss: Sadness and Depression, 1980). Evidence from neurophysiology has grounded such mechanism of infant attachment to the dynamic interplay between an opioid-based proximity-seeking mechanism and an NE-based arousal system that are regulated by external stimuli (interaction with primary caregiver and the environment). Here, we model such attachment mechanism and its dynamic regulation by a coupled system of ordinary differential equations. We simulated the characteristic patterns of infant behaviors in the strange situation procedure, a common instrument for assessing the quality of attachment outcomes (¿types¿) for infants at about one year of age. We also manipulated the parameters of our model to account for neurochemical adaptation, and to allow for caregiver style (such as responsiveness and other factors) and temperamental factor (such as reactivity and readiness in self-regulation) to be incorporated into the homeostatic regulation model of attachment dynamics. Principle component analysis revealed the characteristic regions in the parameter space that correspond to secure, anxious, and avoidant attachment typology. Implications from this kind of approach are discussed.
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