14,595 research outputs found
Behavioural robustness and the distributed mechanisms hypothesis
A current challenge in neuroscience and systems biology is to better understand properties that allow organisms to exhibit and sustain appropriate behaviours despite the effects of perturbations (behavioural robustness). There are still significant theoretical difficulties in this endeavour, mainly due to the context-dependent nature of the problem. Biological robustness, in general, is considered in the literature as a property that emerges from the internal structure of organisms, rather than being a dynamical phenomenon involving agent-internal controls, the organism body, and the environment. Our hypothesis is that the capacity for behavioural robustness is rooted in dynamical processes that are distributed between agent âbrainâ, body, and environment, rather than warranted exclusively by organismsâ internal mechanisms. Distribution is operationally defined here based on perturbation analyses.
Evolutionary Robotics (ER) techniques are used here to construct four computational models to study behavioural robustness from a systemic perspective. Dynamical systems theory provides the conceptual framework for these investigations. The first model evolves situated agents in a goalseeking scenario in the presence of neural noise perturbations. Results suggest that evolution implicitly selects neural systems that are noise-resistant during coupling behaviour by concentrating search in regions of the fitness landscape that retain functionality for goal approaching. The second model evolves situated, dynamically limited agents exhibiting minimalcognitive behaviour (categorization task). Results indicate a small but significant tendency toward better performance under most types of perturbations by agents showing further cognitivebehavioural dependency on their environments. The third model evolves experience-dependent robust behaviour in embodied, one-legged walking agents. Evidence suggests that robustness is rooted in both internal and external dynamics, but robust motion emerges always from the systemin-coupling. The fourth model implements a historically dependent, mobile-object tracking task under sensorimotor perturbations. Results indicate two different modes of distribution, one in which inner controls necessarily depend on a set of specific environmental factors to exhibit behaviour, then these controls will be more vulnerable to perturbations on that set, and another for which these factors are equally sufficient for behaviours. Vulnerability to perturbations depends on the particular distribution.
In contrast to most existing approaches to the study of robustness, this thesis argues that behavioural robustness is better understood in the context of agent-environment dynamical couplings, not in terms of internal mechanisms. Such couplings, however, are not always the full determinants of robustness. Challenges and limitations of our approach are also identified for future studies
Adaptive Regulation in the Amoral Bazaar
Twelfth Oliver Schreiner Memorial Lecture,delivered on 20 October 2010 at the School of Law, University of the Witwatersrand, Johannesburg, South Africa. Many gradual changes in science, law and society are crystallizing to shape a significant transformation in administrative law. The doctrinal framework within which Justice Schreiner himself attempted to modernize how law should regulate government and private economic activity seems from our vantage point to be quite antiquated. In explaining why, my examples will come from the world of financial services, but they could easily be found anywhere in the area of law and regulation. First I will outline the basic premises of prevailing doctrine and its growing shortcomings. Then I will describe developments in our understanding of the social ecologies through which law and regulation is transfused. I will consider some of the implications for the way in which we need to think about future regulation in order to be more effective in this complex world. We are moving from a framework of directive regulation to one that has to become much more adaptive. While my talk will focus on understanding markets as evolutionary social ecologies, and the consequences this has for administrative law and regulation, it is also important that these amoral bazaars be grounded on a foundation of moral aspiraton and integrity. I will therefore conclude with a reminder that we ignore at our peril the urgent responsibility of redeveloping a moral framework within which markets should operate
Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms
The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent âdevicesâ, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew âcognitive devicesâ are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications
On the Evolutionary Co-Adaptation of Morphology and Distributed Neural Controllers in Adaptive Agents
The attempt to evolve complete embodied and situated artiïŹcial creatures in which
both morphological and control characteristics are adapted during the evolutionary
process has been and still represents a long term goal key for the artiïŹcial life and
the evolutionary robotics community.
Loosely inspired by ancient biological organisms which are not provided with a
central nervous system and by simple organisms such as stick insects, this thesis
proposes a new genotype encoding which allows development and evolution of mor-
phology and neural controller in artiïŹcial agents provided with a distributed neural
network.
In order to understand if this kind of network is appropriate for the evolution of
non trivial behaviours in artiïŹcial agents, two experiments (description and results
will be shown in chapter 3) in which evolution was applied only to the controllerâs
parameters were performed.
The results obtained in the ïŹrst experiment demonstrated how distributed neural
networks can achieve a good level of organization by synchronizing the output of
oscillatory elements exploiting acceleration/deceleration mechanisms based on local
interactions.
In the second experiment few variants on the topology of neural architecture were
introduced. Results showed how this new control system was able to coordinate the
legs of a simulated hexapod robot on two diïŹerent gaits on the basis of the external
circumstances.
After this preliminary and successful investigation, a new genotype encoding able to
develop and evolve artiïŹcial agents with no ïŹxed morphology and with a distributed
neural controller was proposed. A second set of experiments was thus performed
and the results obtained conïŹrmed both the eïŹectiveness of genotype encoding and
the ability of distributed neural network to perform the given task.
The results have also shown the strength of genotype both in generating a wide
range of diïŹerent morphological structures and in favouring a direct co-adaptation
between neural controller and morphology during the evolutionary process.
Furthermore the simplicity of the proposed model has showed the eïŹective role of
speciïŹc elements in evolutionary experiments. In particular it has demonstrated the
importance of the environment and its complexity in evolving non-trivial behaviours
and also how adding an independent component to the ïŹtness function could help
the evolutionary process exploring a larger space solutions avoiding a premature
convergence towards suboptimal solutions
What does not happen: quantifying embodied engagement using NIMI and self-adaptors
Previous research into the quantification of embodied intellectual and emotional engagement using non-verbal movement parameters has not yielded consistent results across different studies. Our research introduces NIMI (Non-Instrumental Movement Inhibition) as an alternative parameter. We propose that the absence of certain types of possible movements can be a more holistic proxy for cognitive engagement with media (in seated persons) than searching for the presence of other movements. Rather than analyzing total movement as an indicator of engagement, our research team distinguishes between instrumental movements (i.e. physical movement serving a direct purpose in the given situation) and non-instrumental movements, and investigates them in the context of the narrative rhythm of the stimulus. We demonstrate that NIMI occurs by showing viewersâ movement levels entrained (i.e. synchronised) to the repeating narrative rhythm of a timed computer-presented quiz. Finally, we discuss the role of objective metrics of engagement in future context-aware analysis of human behaviour in audience research, interactive media and responsive system and interface design
Social cognition in the age of humanârobot interaction
Artificial intelligence advances have led to robots endowed with increasingly sophisticated social abilities. These machines speak to our innate desire to perceive social cues in the environment, as well as the promise of robots enhancing our daily lives. However, a strong mismatch still exists between our expectations and the reality of social robots. We argue that careful delineation of the neurocognitive mechanisms supporting humanârobot interaction will enable us to gather insights critical for optimising social encounters between humans and robots. To achieve this, the field must incorporate human neuroscience tools including mobile neuroimaging to explore long-term, embodied humanârobot interaction in situ. New analytical neuroimaging approaches will enable characterisation of social cognition representations on a finer scale using
sensitive and appropriate categorical comparisons (human, animal, tool, or
object). The future of social robotics is undeniably exciting, and insights from human neuroscience research will bring us closer to interacting and collaborating with socially sophisticated robots
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