31 research outputs found
Comparing reliability of grid-based Quality-Diversity algorithms using artificial landscapes
Quality-Diversity (QD) algorithms are a recent type of optimisation methods
that search for a collection of both diverse and high performing solutions.
They can be used to effectively explore a target problem according to features
defined by the user. However, the field of QD still does not possess extensive
methodologies and reference benchmarks to compare these algorithms. We propose
a simple benchmark to compare the reliability of QD algorithms by optimising
the Rastrigin function, an artificial landscape function often used to test
global optimisation methods.Comment: 3 pages, 2 figure
Investor-patent networks as mutualistic networks
Venture capital investments in startups have come to represent an important
driver of technological innovation, in parallel to corporate- and
government-directed efforts. Part of the future of artificial intelligence,
medicine and quantum computing now depends upon a large number of venture
investment decisions whose robustness against increasingly frequent crises has
therefore become crucial. To shed light on this issue, and by combining
large-scale financial, startup and patent datasets, we analyze the interactions
between venture capitalists and technologies as an explicit bipartite
patent-investor network. Our results reveal that this network is topologically
mutualistic because of the prevalence of links between generalist investors,
whose portfolios are technologically diversified, and general-purpose
technologies, characterized by a broad spectrum of use. As a consequence, the
robustness of venture-funded technological innovation against different types
of crises is affected by the high nestedness and low modularity, with high
connectance, associated with mutualistic networks.Comment: 16 pages with appendix, 4 figures, 2 table
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
Robot Collection and Transport of Objects: A Biomimetic Process
Animals as diverse as ants and humans are faced with the tasks of collecting, transporting or herding objects. Sheepdogs do this daily when they collect, herd, and maneuver flocks of sheep. Here, we adapt a shepherding algorithm inspired by sheepdogs to collect and transport objects using a robot. Our approach produces an effective robot collection process that autonomously adapts to changing environmental conditions and is robust to noise from various sources. We suggest that this biomimetic process could be implemented into suitable robots to perform collection and transport tasks that might include – for example – cleaning up objects in the environment, keeping animals away from sensitive areas or collecting and herding animals to a specific location. Furthermore, the feedback controlled interactions between the robot and objects which we study can be used to interrogate and understand the local and global interactions of real animal groups, thus offering a novel methodology of value to researchers studying collective animal behavior
Evolutionary optimisation of neural network models for fish collective behaviours in mixed groups of robots and zebrafish
Animal and robot social interactions are interesting both for ethological
studies and robotics. On the one hand, the robots can be tools and models to
analyse animal collective behaviours, on the other hand, the robots and their
artificial intelligence are directly confronted and compared to the natural
animal collective intelligence. The first step is to design robots and their
behavioural controllers that are capable of socially interact with animals.
Designing such behavioural bio-mimetic controllers remains an important
challenge as they have to reproduce the animal behaviours and have to be
calibrated on experimental data. Most animal collective behavioural models are
designed by modellers based on experimental data. This process is long and
costly because it is difficult to identify the relevant behavioural features
that are then used as a priori knowledge in model building. Here, we want to
model the fish individual and collective behaviours in order to develop robot
controllers. We explore the use of optimised black-box models based on
artificial neural networks (ANN) to model fish behaviours. While the ANN may
not be biomimetic but rather bio-inspired, they can be used to link perception
to motor responses. These models are designed to be implementable as robot
controllers to form mixed-groups of fish and robots, using few a priori
knowledge of the fish behaviours. We present a methodology with multilayer
perceptron or echo state networks that are optimised through evolutionary
algorithms to model accurately the fish individual and collective behaviours in
a bounded rectangular arena. We assess the biomimetism of the generated models
and compare them to the fish experimental behaviours.Comment: 10 pages, 4 figure