118,048 research outputs found
Evolutionary Robotics: a new scientific tool for studying cognition
We survey developments in Artificial Neural Networks, in Behaviour-based Robotics and Evolutionary Algorithms that set the stage for Evolutionary Robotics in the 1990s. We examine the motivations for using ER as a scientific tool for studying minimal models of cognition, with the advantage of being capable of generating integrated sensorimotor systems with minimal (or controllable) prejudices. These systems must act as a whole in close coupling with their environments which is an essential aspect of real cognition that is often either bypassed or modelled poorly in other disciplines. We demonstrate with three example studies: homeostasis under visual inversion; the origins of learning; and the ontogenetic acquisition of entrainment
Embodied Artificial Intelligence through Distributed Adaptive Control: An Integrated Framework
In this paper, we argue that the future of Artificial Intelligence research
resides in two keywords: integration and embodiment. We support this claim by
analyzing the recent advances of the field. Regarding integration, we note that
the most impactful recent contributions have been made possible through the
integration of recent Machine Learning methods (based in particular on Deep
Learning and Recurrent Neural Networks) with more traditional ones (e.g.
Monte-Carlo tree search, goal babbling exploration or addressable memory
systems). Regarding embodiment, we note that the traditional benchmark tasks
(e.g. visual classification or board games) are becoming obsolete as
state-of-the-art learning algorithms approach or even surpass human performance
in most of them, having recently encouraged the development of first-person 3D
game platforms embedding realistic physics. Building upon this analysis, we
first propose an embodied cognitive architecture integrating heterogenous
sub-fields of Artificial Intelligence into a unified framework. We demonstrate
the utility of our approach by showing how major contributions of the field can
be expressed within the proposed framework. We then claim that benchmarking
environments need to reproduce ecologically-valid conditions for bootstrapping
the acquisition of increasingly complex cognitive skills through the concept of
a cognitive arms race between embodied agents.Comment: Updated version of the paper accepted to the ICDL-Epirob 2017
conference (Lisbon, Portugal
Modelling Adaptation through Social Allostasis: Modulating the Effects of Social Touch with Oxytocin in Embodied Agents
Social allostasis is a mechanism of adaptation that permits individuals to dynamically adapt their physiology to changing physical and social conditions. Oxytocin (OT) is widely considered to be one of the hormones that drives and adapts social behaviours. While its precise effects remain unclear, two areas where OT may promote adaptation are by affecting social salience, and affecting internal responses of performing social behaviours. Working towards a model of dynamic adaptation through social allostasis in simulated embodied agents, and extending our previous work studying OT-inspired modulation of social salience, we present a model and experiments that investigate the effects and adaptive value of allostatic processes based on hormonal (OT) modulation of affective elements of a social behaviour. In particular, we investigate and test the effects and adaptive value of modulating the degree of satisfaction of tactile contact in a social motivation context in a small simulated agent society across different environmental challenges (related to availability of food) and effects of OT modulation of social salience as a motivational incentive. Our results show that the effects of these modulatory mechanisms have different (positive or negative) adaptive value across different groups and under different environmental circumstance in a way that supports the context-dependent nature of OT, put forward by the interactionist approach to OT modulation in biological agents. In terms of simulation models, this means that OT modulation of the mechanisms that we have described should be context-dependent in order to maximise viability of our socially adaptive agents, illustrating the relevance of social allostasis mechanisms.Peer reviewedFinal Published versio
Controllability of Social Networks and the Strategic Use of Random Information
This work is aimed at studying realistic social control strategies for social
networks based on the introduction of random information into the state of
selected driver agents. Deliberately exposing selected agents to random
information is a technique already experimented in recommender systems or
search engines, and represents one of the few options for influencing the
behavior of a social context that could be accepted as ethical, could be fully
disclosed to members, and does not involve the use of force or of deception.
Our research is based on a model of knowledge diffusion applied to a
time-varying adaptive network, and considers two well-known strategies for
influencing social contexts. One is the selection of few influencers for
manipulating their actions in order to drive the whole network to a certain
behavior; the other, instead, drives the network behavior acting on the state
of a large subset of ordinary, scarcely influencing users. The two approaches
have been studied in terms of network and diffusion effects. The network effect
is analyzed through the changes induced on network average degree and
clustering coefficient, while the diffusion effect is based on two ad-hoc
metrics defined to measure the degree of knowledge diffusion and skill level,
as well as the polarization of agent interests. The results, obtained through
simulations on synthetic networks, show a rich dynamics and strong effects on
the communication structure and on the distribution of knowledge and skills,
supporting our hypothesis that the strategic use of random information could
represent a realistic approach to social network controllability, and that with
both strategies, in principle, the control effect could be remarkable
Learning obstacle avoidance with an operant behavioral model
Artificial intelligence researchers have been attracted by the idea of having robots learn how to accomplish a task, rather than being told explicitly. Reinforcement learning has been proposed as an appealing framework to be used in controlling mobile agents. Robot learning research, as well as research in biological systems, face many similar problems in order to display high flexibility in performing a variety of tasks. In this work, the controlling of a vehicle in an avoidance task by a previously developed operant learning model (a form of animal learning) is studied. An environment in which a mobile robot with proximity sensors has to minimize the punishment for colliding against obstacles is simulated. The results were compared with the Q-Learning algorithm, and the proposed model had better performance. In this way a new artificial intelligence agent inspired by neurobiology, psychology, and ethology research is proposed.Fil: Gutnisky, D. A.. Universidad de Buenos Aires. Facultad de IngenierĂa.Instituto de IngenierĂa BiomĂ©dica; ArgentinaFil: Zanutto, Bonifacio Silvano. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Instituto de BiologĂa y Medicina Experimental. FundaciĂłn de Instituto de BiologĂa y Medicina Experimental. Instituto de BiologĂa y Medicina Experimental; Argentina. Universidad de Buenos Aires. Facultad de IngenierĂa.Instituto de IngenierĂa BiomĂ©dica; Argentin
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