146 research outputs found
Development of Cognitive Capabilities in Humanoid Robots
Merged with duplicate record 10026.1/645 on 03.04.2017 by CS (TIS)Building intelligent systems with human level of competence is the ultimate
grand challenge for science and technology in general, and especially for the
computational intelligence community. Recent theories in autonomous cognitive
systems have focused on the close integration (grounding) of communication with
perception, categorisation and action. Cognitive systems are essential for
integrated multi-platform systems that are capable of sensing and communicating.
This thesis presents a cognitive system for a humanoid robot that integrates
abilities such as object detection and recognition, which are merged with natural
language understanding and refined motor controls. The work includes three
studies; (1) the use of generic manipulation of objects using the NMFT algorithm,
by successfully testing the extension of the NMFT to control robot behaviour; (2) a
study of the development of a robotic simulator; (3) robotic simulation experiments
showing that a humanoid robot is able to acquire complex behavioural, cognitive,
and linguistic skills through individual and social learning. The robot is able to
learn to handle and manipulate objects autonomously, to cooperate with human
users, and to adapt its abilities to changes in internal and environmental conditions.
The model and the experimental results reported in this thesis, emphasise the
importance of embodied cognition, i.e. the humanoid robot's physical interaction
between its body and the environment
Music as complex emergent behaviour : an approach to interactive music systems
Access to the full-text thesis is no longer available at the author's request, due to 3rd party copyright restrictions. Access removed on 28.11.2016 by CS (TIS).Metadata merged with duplicate record (http://hdl.handle.net/10026.1/770) on 20.12.2016 by CS (TIS).This is a digitised version of a thesis that was deposited in the University Library. If you are the author please contact PEARL Admin ([email protected]) to discuss options.This thesis suggests a new model of human-machine interaction in the domain of non-idiomatic
musical improvisation. Musical results are viewed as emergent phenomena
issuing from complex internal systems behaviour in relation to input from a single
human performer. We investigate the prospect of rewarding interaction whereby a
system modifies itself in coherent though non-trivial ways as a result of exposure to a
human interactor. In addition, we explore whether such interactions can be sustained
over extended time spans. These objectives translate into four criteria for evaluation;
maximisation of human influence, blending of human and machine influence in the
creation of machine responses, the maintenance of independent machine motivations
in order to support machine autonomy and finally, a combination of global emergent
behaviour and variable behaviour in the long run. Our implementation is heavily
inspired by ideas and engineering approaches from the discipline of Artificial Life.
However, we also address a collection of representative existing systems from the
field of interactive composing, some of which are implemented using techniques of
conventional Artificial Intelligence. All systems serve as a contextual background and
comparative framework helping the assessment of the work reported here.
This thesis advocates a networked model incorporating functionality for listening,
playing and the synthesis of machine motivations. The latter incorporate dynamic
relationships instructing the machine to either integrate with a musical context
suggested by the human performer or, in contrast, perform as an individual musical
character irrespective of context. Techniques of evolutionary computing are used to
optimise system components over time. Evolution proceeds based on an implicit
fitness measure; the melodic distance between consecutive musical statements made
by human and machine in relation to the currently prevailing machine motivation.
A substantial number of systematic experiments reveal complex emergent behaviour
inside and between the various systems modules. Music scores document how global
systems behaviour is rendered into actual musical output. The concluding chapter
offers evidence of how the research criteria were accomplished and proposes
recommendations for future research
The Social Cognitive Actor
Multi-Agent Simulation (MAS) of organisations is a methodology that is adopted in this dissertation in order to study and understand human behaviour in organisations.
The aim of the research is to design and implementat a cognitive and social multi-agent simulation model based on a selection of social and cognitive theories to fulfill the need for a complex cognitive and social model. The emphasis of this dissertation is the relationship between behaviour of individuals (micro-level) in an organisation and the behaviour of the organisation as a whole (macro-level)
Where is cognition? Towards an embodied, situated, and distributed interactionist theory of cognitive activity
In recent years researchers from a variety of cognitive science disciplines have begun to challenge some of the core assumptions of the dominant theoretical framework of cognitivism including the representation-computational view of cognition, the sense-model-plan-act understanding of cognitive architecture, and the use of a formal task description strategy for investigating the organisation of internal mental processes. Challenges to these assumptions are illustrated using empirical findings and theoretical arguments from the fields such as situated robotics, dynamical systems approaches to cognition, situated action and distributed cognition research, and sociohistorical studies of cognitive development. Several shared themes are extracted from the findings in these research programmes including: a focus on agent-environment systems as the primary unit of analysis; an attention to agent-environment interaction dynamics; a vision of the cognizer's internal mechanisms as essentially reactive and decentralised in nature; and a tendency for mutual definitions of agent, environment, and activity. It is argued that, taken together, these themes signal the emergence of a new approach to cognition called embodied, situated, and distributed interactionism. This interactionist alternative has many resonances with the dynamical systems approach to cognition. However, this approach does not provide a theory of the implementing substrate sufficient for an interactionist theoretical framework. It is suggested that such a theory can be found in a view of animals as autonomous systems coupled with a portrayal of the nervous system as a regulatory, coordinative, and integrative bodily subsystem. Although a number of recent simulations show connectionism's promise as a computational technique in simulating the role of the nervous system from an interactionist perspective, this embodied connectionist framework does not lend itself to understanding the advanced 'representation hungry' cognition we witness in much human behaviour. It is argued that this problem can be solved by understanding advanced cognition as the re-use of basic perception-action skills and structures that this feat is enabled by a general education within a social symbol-using environment
Drama, a connectionist model for robot learning: experiments on grounding communication through imitation in autonomous robots
The present dissertation addresses problems related to robot learning from demonstra¬
tion. It presents the building of a connectionist architecture, which provides the robot
with the necessary cognitive and behavioural mechanisms for learning a synthetic lan¬
guage taught by an external teacher agent. This thesis considers three main issues:
1) learning of spatio-temporal invariance in a dynamic noisy environment, 2) symbol
grounding of a robot's actions and perceptions, 3) development of a common symbolic
representation of the world by heterogeneous agents.We build our approach on the assumption that grounding of symbolic communication
creates constraints not only on the cognitive capabilities of the agent but also and especially on its behavioural capacities. Behavioural skills, such as imitation, which allow
the agent to co-ordinate its actionn to that of the teacher agent, are required aside to
general cognitive abilities of associativity, in order to constrain the agent's attention
to making relevant perceptions, onto which it grounds the teacher agent's symbolic
expression. In addition, the agent should be provided with the cognitive capacity for
extracting spatial and temporal invariance in the continuous flow of its perceptions.
Based on this requirement, we develop a connectionist architecture for learning time
series. The model is a Dynamical Recurrent Associative Memory Architecture, called
DRAMA. It is a fully connected recurrent neural network using Hebbian update rules.
Learning is dynamic and unsupervised. The performance of the architecture is analysed theoretically, through numerical simulations and through physical and simulated
robotic experiments. Training of the network is computationally fast and inexpensive,
which allows its implementation for real time computation and on-line learning in a
inexpensive hardware system. Robotic experiments are carried out with different learning tasks involving recognition of spatial and temporal invariance, namely landmark
recognition and prediction of perception-action sequence in maze travelling.The architecture is applied to experiments on robot learning by imitation. A learner
robot is taught by a teacher agent, a human instructor and another robot, a vocabulary
to describe its perceptions and actions. The experiments are based on an imitative
strategy, whereby the learner robot reproduces the teacher's actions. While imitating
the teacher's movements, the learner robot makes similar proprio and exteroceptions
to those of the teacher. The learner robot grounds the teacher's words onto the set of
common perceptions they share. We carry out experiments in simulated and physical
environments, using different robotic set-ups, increasing gradually the complexity of
the task. In a first set of experiments, we study transmission of a vocabulary to
designate actions and perception of a robot. Further, we carry out simulation studies,
in which we investigate transmission and use of the vocabulary among a group of
robotic agents. In a third set of experiments, we investigate learning sequences of the
robot's perceptions, while wandering in a physically constrained environment. Finally,
we present the implementation of DRAMA in Robota, a doll-like robot, which can
imitate the arms and head movements of a human instructor. Through this imitative
game, Robota is taught to perform and label dance patterns. Further, Robota is taught
a basic language, including a lexicon and syntactical rules for the combination of words
of the lexicon, to describe its actions and perception of touch onto its body
Spectators’ aesthetic experiences of sound and movement in dance performance
In this paper we present a study of spectators’ aesthetic experiences of sound and movement in live dance performance. A multidisciplinary team comprising a choreographer, neuroscientists and qualitative researchers investigated the effects of different sound scores on dance spectators. What would be the impact of auditory stimulation on kinesthetic experience and/or aesthetic appreciation of the dance? What would be the effect of removing music altogether, so that spectators watched dance while hearing only the performers’ breathing and footfalls? We investigated audience experience through qualitative research, using post-performance focus groups, while a separately conducted functional brain imaging (fMRI) study measured the synchrony in brain activity across spectators when they watched dance with sound or breathing only. When audiences watched dance accompanied by music the fMRI data revealed evidence of greater intersubject synchronisation in a brain region consistent with complex auditory processing. The audience research found that some spectators derived pleasure from finding convergences between two complex stimuli (dance and music). The removal of music and the resulting audibility of the performers’ breathing had a significant impact on spectators’ aesthetic experience. The fMRI analysis showed increased synchronisation among observers, suggesting greater influence of the body when interpreting the dance stimuli. The audience research found evidence of similar corporeally focused experience. The paper discusses possible connections between the findings of our different approaches, and considers the implications of this study for interdisciplinary research collaborations between arts and sciences
The 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies
This publication comprises the papers presented at the 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies held at the NASA/Goddard Space Flight Center, Greenbelt, Maryland, on May 9-11, 1995. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed
Simulating Humans: Computer Graphics, Animation, and Control
People are all around us. They inhabit our home, workplace, entertainment, and environment. Their presence and actions are noted or ignored, enjoyed or disdained, analyzed or prescribed. The very ubiquitousness of other people in our lives poses a tantalizing challenge to the computational modeler: people are at once the most common object of interest and yet the most structurally complex. Their everyday movements are amazingly uid yet demanding to reproduce, with actions driven not just mechanically by muscles and bones but also cognitively by beliefs and intentions. Our motor systems manage to learn how to make us move without leaving us the burden or pleasure of knowing how we did it. Likewise we learn how to describe the actions and behaviors of others without consciously struggling with the processes of perception, recognition, and language
Interaction dynamics and autonomy in cognitive systems
The concept of autonomy is of crucial importance for understanding life and cognition. Whereas cellular and organismic autonomy is based in the self-production of the material infrastructure sustaining the existence of living beings as such, we are interested in how biological autonomy can be expanded into forms of autonomous agency, where autonomy as a form of organization is extended into the behaviour of an agent in interaction with its environment (and not its material self-production). In this thesis, we focus on the development of operational models of sensorimotor agency, exploring the construction of a domain of interactions creating a dynamical interface between agent and environment. We present two main contributions to the study of autonomous agency: First, we contribute to the development of a modelling route for testing, comparing and validating hypotheses about neurocognitive autonomy. Through the design and analysis of specific neurodynamical models embedded in robotic agents, we explore how an agent is constituted in a sensorimotor space as an autonomous entity able to adaptively sustain its own organization. Using two simulation models and different dynamical analysis and measurement of complex patterns in their behaviour, we are able to tackle some theoretical obstacles preventing the understanding of sensorimotor autonomy, and to generate new predictions about the nature of autonomous agency in the neurocognitive domain. Second, we explore the extension of sensorimotor forms of autonomy into the social realm. We analyse two cases from an experimental perspective: the constitution of a collective subject in a sensorimotor social interactive task, and the emergence of an autonomous social identity in a large-scale technologically-mediated social system. Through the analysis of coordination mechanisms and emergent complex patterns, we are able to gather experimental evidence indicating that in some cases social autonomy might emerge based on mechanisms of coordinated sensorimotor activity and interaction, constituting forms of collective autonomous agency
Artificial Intelligence Research Branch future plans
This report contains information on the activities of the Artificial Intelligence Research Branch (FIA) at NASA Ames Research Center (ARC) in 1992, as well as planned work in 1993. These activities span a range from basic scientific research through engineering development to fielded NASA applications, particularly those applications that are enabled by basic research carried out in FIA. Work is conducted in-house and through collaborative partners in academia and industry. All of our work has research themes with a dual commitment to technical excellence and applicability to NASA short, medium, and long-term problems. FIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at the Jet Propulsion Laboratory (JPL) and AI applications groups throughout all NASA centers. This report is organized along three major research themes: (1) Planning and Scheduling: deciding on a sequence of actions to achieve a set of complex goals and determining when to execute those actions and how to allocate resources to carry them out; (2) Machine Learning: techniques for forming theories about natural and man-made phenomena; and for improving the problem-solving performance of computational systems over time; and (3) Research on the acquisition, representation, and utilization of knowledge in support of diagnosis design of engineered systems and analysis of actual systems
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