1,866 research outputs found
Embodied Evolution in Collective Robotics: A Review
This paper provides an overview of evolutionary robotics techniques applied
to on-line distributed evolution for robot collectives -- namely, embodied
evolution. It provides a definition of embodied evolution as well as a thorough
description of the underlying concepts and mechanisms. The paper also presents
a comprehensive summary of research published in the field since its inception
(1999-2017), providing various perspectives to identify the major trends. In
particular, we identify a shift from considering embodied evolution as a
parallel search method within small robot collectives (fewer than 10 robots) to
embodied evolution as an on-line distributed learning method for designing
collective behaviours in swarm-like collectives. The paper concludes with a
discussion of applications and open questions, providing a milestone for past
and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl
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Using ODL and ICT to develop the skills of the unreached: a contribution to the ADEA triennial of the Working Group on Distance Education and Open Learning
Innovation in technology is occurring at rapid pace thus shrinking the distances and making information and knowledge more than ever accessible to everyone irrespective of where the person resides. This paper consists of four main articles. The first one deals with technological trends. The second one focuses on the deployment and use of open and distance education mode in rural areas by documenting initiatives that embrace information and communication technologies (ICTs). Due to challenges faced in rural areas only a few success stories/cases currently exist and some of these are cited in this article. The challenges faced in the deployment of ICT enhanced ODL have been highlighted as well as the potential of developing and delivering effective and relevant ODL programmes in rural areas in order to ensure that issues of educational equity and social exclusion rural communities are adequately addressed. ICTs in ODL are perhaps the greatest tool to date for self-education and value addition to any communityâs development efforts, yet poor rural communities particularly in Africa do not have the necessary awareness, skills or facilities to enable themselves to develop using ICTs. Inadequate ICT infrastructures in rural areas remain a major source for the digital divide in Africa and for under-performance of distance learners. The third one analyses the support provided to ODL learners who often encounter difficulties in completing their studies through the distance education mode due to loneliness, uncertainties and de-motivation. ICT has not been able to sufficiently support distance learners in overcoming those obstacles efficiently. An investigation regarding those learning supports has been conducted in ten distance learning institutions, along with an intensive literature review with the aim of understanding the high percentage of dropout rates of distant learners. The learnersâ interactions have been scrutinized through content analysis of their synchronous exchanges, during a completely online course. After taking into account the limited technical and human resources in Africa, a technological virtual environment along with a pedagogical framework has been proposed with the aim of giving adequate educational support to them. The fourth article has explored The Open University (UK) and its efforts to use new technologies to deliver online courses to difficult-to- reach learners in prison environments. The case study analysed here is an international course (called, B201- Business Organisations and their environments) which also touches an African cohort of learners. The implications for designing and delivering online ODL to the complex unreachable environments of prisons anywhere, and particularly in Africa, have been discussed
Gaussian belief propagation for real-time decentralised inference
For embodied agents to interact intelligently with their surroundings, they require perception systems that construct persistent 3D representations of their environments. These representations must be rich; capturing 3D geometry, semantics, physical properties, affordances and much more. Constructing the environment representation from sensory observations is done via Bayesian probabilistic inference and in practical systems, inference must take place within the power, compactness and simplicity constraints of real products. Efficient inference within these constraints however remains computationally challenging and current systems often require heavy computational resources while delivering a fraction of the desired capabilities.
Decentralised algorithms based on local message passing with in-place processing and storage offer a promising solution to current inference bottlenecks. They are well suited to take advantage of recent rapid developments in distributed asynchronous processing hardware to achieve efficient, scalable and low-power performance.
In this thesis, we argue for Gaussian belief propagation (GBP) as a strong algorithmic framework for distributed, generic and incremental probabilistic estimation. GBP operates by passing messages between the nodes on a factor graph and can converge with arbitrary asynchronous message schedules. We envisage the factor graph being the fundamental master environment representation, and GBP the flexible inference tool to compute local in-place probabilistic estimates. In large real-time systems, GBP will act as the `glue' between specialised modules, with attention based processing bringing about local convergence in the graph in a just-in-time manner.
This thesis contains several technical and theoretical contributions in the application of GBP to practical real-time inference problems in vision and robotics. Additionally, we implement GBP on novel graph processor hardware and demonstrate breakthrough speeds for bundle adjustment problems. Lastly, we present a prototype system for incrementally creating hierarchical abstract scene graphs by combining neural networks and probabilistic inference via GBP.Open Acces
Digital Ecosystems: Ecosystem-Oriented Architectures
We view Digital Ecosystems to be the digital counterparts of biological
ecosystems. Here, we are concerned with the creation of these Digital
Ecosystems, exploiting the self-organising properties of biological ecosystems
to evolve high-level software applications. Therefore, we created the Digital
Ecosystem, a novel optimisation technique inspired by biological ecosystems,
where the optimisation works at two levels: a first optimisation, migration of
agents which are distributed in a decentralised peer-to-peer network, operating
continuously in time; this process feeds a second optimisation based on
evolutionary computing that operates locally on single peers and is aimed at
finding solutions to satisfy locally relevant constraints. The Digital
Ecosystem was then measured experimentally through simulations, with measures
originating from theoretical ecology, evaluating its likeness to biological
ecosystems. This included its responsiveness to requests for applications from
the user base, as a measure of the ecological succession (ecosystem maturity).
Overall, we have advanced the understanding of Digital Ecosystems, creating
Ecosystem-Oriented Architectures where the word ecosystem is more than just a
metaphor.Comment: 39 pages, 26 figures, journa
A tutorial on optimization for multi-agent systems
Research on optimization in multi-agent systems (MASs) has contributed with a wealth of techniques to solve many of the challenges arising in a wide range of multi-agent application domains. Multi-agent optimization focuses on casting MAS problems into optimization problems. The solving of those problems could possibly involve the active participation of the agents in a MAS. Research on multi-agent optimization has rapidly become a very technical, specialized field. Moreover, the contributions to the field in the literature are largely scattered. These two factors dramatically hinder access to a basic, general view of the foundations of the field. This tutorial is intended to ease such access by providing a gentle introduction to fundamental concepts and techniques on multi-agent optimization. © 2013 The Author.Peer Reviewe
A feedback-based decentralised coordination model for distributed open real-time systems
Moving towards autonomous operation and management of increasingly
complex open distributed real-time systems poses very significant challenges.
This is particularly true when reaction to events must be done in a timely and
predictable manner while guaranteeing Quality of Service (QoS) constraints
imposed by users, the environment, or applications. In these scenarios, the
system should be able to maintain a global feasible QoS level while allowing
individual nodes to autonomously adapt under different constraints of
resource availability and input quality.
This paper shows how decentralised coordination of a group of autonomous
interdependent nodes can emerge with little communication, based on the
robust self-organising principles of feedback. Positive feedback is used to
reinforce the selection of the new desired global service solution, while negative
feedback discourages nodes to act in a greedy fashion as this adversely
impacts on the provided service levels at neighbouring nodes.
The proposed protocol is general enough to be used in a wide range of
scenarios characterised by a high degree of openness and dynamism where coordination
tasks need to be time dependent. As the reported results demonstrate,
it requires less messages to be exchanged and it is faster to achieve a
globally acceptable near-optimal solution than other available approaches
Engineering Resilient Collective Adaptive Systems by Self-Stabilisation
Collective adaptive systems are an emerging class of networked computational
systems, particularly suited in application domains such as smart cities,
complex sensor networks, and the Internet of Things. These systems tend to
feature large scale, heterogeneity of communication model (including
opportunistic peer-to-peer wireless interaction), and require inherent
self-adaptiveness properties to address unforeseen changes in operating
conditions. In this context, it is extremely difficult (if not seemingly
intractable) to engineer reusable pieces of distributed behaviour so as to make
them provably correct and smoothly composable.
Building on the field calculus, a computational model (and associated
toolchain) capturing the notion of aggregate network-level computation, we
address this problem with an engineering methodology coupling formal theory and
computer simulation. On the one hand, functional properties are addressed by
identifying the largest-to-date field calculus fragment generating
self-stabilising behaviour, guaranteed to eventually attain a correct and
stable final state despite any transient perturbation in state or topology, and
including highly reusable building blocks for information spreading,
aggregation, and time evolution. On the other hand, dynamical properties are
addressed by simulation, empirically evaluating the different performances that
can be obtained by switching between implementations of building blocks with
provably equivalent functional properties. Overall, our methodology sheds light
on how to identify core building blocks of collective behaviour, and how to
select implementations that improve system performance while leaving overall
system function and resiliency properties unchanged.Comment: To appear on ACM Transactions on Modeling and Computer Simulatio
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