1,861 research outputs found
Automatic Calibration of Artificial Neural Networks for Zebrafish Collective Behaviours using a Quality Diversity Algorithm
During the last two decades, various models have been proposed for fish
collective motion. These models are mainly developed to decipher the biological
mechanisms of social interaction between animals. They consider very simple
homogeneous unbounded environments and it is not clear that they can simulate
accurately the collective trajectories. Moreover when the models are more
accurate, the question of their scalability to either larger groups or more
elaborate environments remains open. This study deals with learning how to
simulate realistic collective motion of collective of zebrafish, using
real-world tracking data. The objective is to devise an agent-based model that
can be implemented on an artificial robotic fish that can blend into a
collective of real fish. We present a novel approach that uses Quality
Diversity algorithms, a class of algorithms that emphasise exploration over
pure optimisation. In particular, we use CVT-MAP-Elites, a variant of the
state-of-the-art MAP-Elites algorithm for high dimensional search space.
Results show that Quality Diversity algorithms not only outperform classic
evolutionary reinforcement learning methods at the macroscopic level (i.e.
group behaviour), but are also able to generate more realistic biomimetic
behaviours at the microscopic level (i.e. individual behaviour).Comment: 8 pages, 4 figures, 1 tabl
Stability of Evolving Multi-Agent Systems
A Multi-Agent System is a distributed system where the agents or nodes
perform complex functions that cannot be written down in analytic form.
Multi-Agent Systems are highly connected, and the information they contain is
mostly stored in the connections. When agents update their state, they take
into account the state of the other agents, and they have access to those
states via the connections. There is also external, user-generated input into
the Multi-Agent System. As so much information is stored in the connections,
agents are often memory-less. This memory-less property, together with the
randomness of the external input, has allowed us to model Multi-Agent Systems
using Markov chains. In this paper, we look at Multi-Agent Systems that evolve,
i.e. the number of agents varies according to the fitness of the individual
agents. We extend our Markov chain model, and define stability. This is the
start of a methodology to control Multi-Agent Systems. We then build upon this
to construct an entropy-based definition for the degree of instability (entropy
of the limit probabilities), which we used to perform a stability analysis. We
then investigated the stability of evolving agent populations through
simulation, and show that the results are consistent with the original
definition of stability in non-evolving Multi-Agent Systems, proposed by Chli
and De Wilde. This paper forms the theoretical basis for the construction of
Digital Business Ecosystems, and applications have been reported elsewhere.Comment: 9 pages, 5 figures, journa
Optimisation of Mobile Communication Networks - OMCO NET
The mini conference āOptimisation of Mobile Communication Networksā focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University.
The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing
Digital ecosystems
We view Digital Ecosystems to be the digital counterparts of biological ecosystems, which
are considered to be robust, self-organising and scalable architectures that can automatically
solve complex, dynamic problems. So, this work is concerned with the creation, investigation,
and optimisation of Digital Ecosystems, exploiting the self-organising properties of biological
ecosystems. First, 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. We then investigated its self-organising aspects, starting with an extension
to the definition of Physical Complexity to include the evolving agent populations of our
Digital Ecosystem. Next, we established stability of evolving agent populations over time,
by extending the Chli-DeWilde definition of agent stability to include evolutionary dynamics.
Further, we evaluated the diversity of the software agents within evolving agent populations,
relative to the environment provided by the user base. To conclude, we considered alternative
augmentations to optimise and accelerate our Digital Ecosystem, by studying the accelerating
effect of a clustering catalyst on the evolutionary dynamics of our Digital Ecosystem, through
the direct acceleration of the evolutionary processes. We also studied the optimising effect of
targeted migration on the ecological dynamics of our Digital Ecosystem, through the indirect
and emergent optimisation of the agent migration patterns. Overall, we have advanced the
understanding of creating Digital Ecosystems, the self-organisation that occurs within them,
and the optimisation of their Ecosystem-Oriented Architecture
Multi-Agent Communication and Collaboration
Degree: Master of Science in Engineering
Department: EngineeringMulti-Agent Systems are becoming a popular paradigm for many engineering applications. However, there is still much research to be performed in this fast growing field. In this thesis, the effect of learning in multi-agent systems on communication and
collaboration between agents is investigated. This research focuses on agents learning local cooperative behaviour from a centralised agent, as well as using learning to reduce the amount of communication between agents that use negotiation to achieve their goals.
A simple test problem is formulated in MATLAB. The effect of learning is clearly seen to reduce the amount of communication between agents by up to 50%, while still
maintaining cooperative behaviour. The agents are also demonstrated to learn to a large degree cooperative local behaviour from a central system
QoS routing in ad-hoc networks using GA and multi-objective optimization
Much work has been done on routing in Ad-hoc networks, but the proposed routing solutions only deal with the best effort data traffic. Connections with Quality of Service (QoS) requirements, such as voice channels with delay and bandwidth constraints, are not supported. The QoS routing has been receiving increasingly intensive attention, but searching for the shortest path with many metrics is an NP-complete problem. For this reason, approximated solutions and heuristic algorithms should be developed for multi-path constraints QoS routing. Also, the routing methods should be adaptive, flexible, and intelligent. In this paper, we use Genetic Algorithms (GAs) and multi-objective optimization for QoS routing in Ad-hoc Networks. In order to reduce the search space of GA, we implemented a search space reduction algorithm, which reduces the search space for GAMAN (GA-based routing algorithm for Mobile Ad-hoc Networks) to find a new route. We evaluate the performance of GAMAN by computer simulations and show that GAMAN has better behaviour than GLBR (Genetic Load Balancing Routing).Peer ReviewedPostprint (published version
Smart Grid Technologies in Europe: An Overview
The old electricity network infrastructure has proven to be inadequate, with respect to modern challenges such as alternative energy sources, electricity demand and energy saving policies. Moreover, Information and Communication Technologies (ICT) seem to have reached an adequate level of reliability and flexibility in order to support a new concept of electricity networkāthe smart grid. In this work, we will analyse the state-of-the-art of smart grids, in their technical, management, security, and optimization aspects. We will also provide a brief overview of the regulatory aspects involved in the development of a smart grid, mainly from the viewpoint of the European Unio
- ā¦