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Advanced Concepts Team

About: Advanced Concepts Team is a based out in . It is known for research contribution in the topics: Global optimization & Spacecraft. The organization has 90 authors who have published 185 publications receiving 3214 citations.


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Journal ArticleDOI
TL;DR: This article surveys the dynamical models that have been derived for various multi-agent reinforcement learning algorithms, making it possible to study and compare them qualitatively, and provides a roadmap on the progress that has been achieved in analysing the evolutionary dynamics of multi- agent learning.
Abstract: The interaction of multiple autonomous agents gives rise to highly dynamic and nondeterministic environments, contributing to the complexity in applications such as automated financial markets, smart grids, or robotics. Due to the sheer number of situations that may arise, it is not possible to foresee and program the optimal behaviour for all agents beforehand. Consequently, it becomes essential for the success of the system that the agents can learn their optimal behaviour and adapt to new situations or circumstances. The past two decades have seen the emergence of reinforcement learning, both in single and multi-agent settings, as a strong, robust and adaptive learning paradigm. Progress has been substantial, and a wide range of algorithms are now available. An important challenge in the domain of multi-agent learning is to gain qualitative insights into the resulting system dynamics. In the past decade, tools and methods from evolutionary game theory have been successfully employed to study multi-agent learning dynamics formally in strategic interactions. This article surveys the dynamical models that have been derived for various multi-agent reinforcement learning algorithms, making it possible to study and compare them qualitatively. Furthermore, new learning algorithms that have been introduced using these evolutionary game theoretic tools are reviewed. The evolutionary models can be used to study complex strategic interactions. Examples of such analysis are given for the domains of automated trading in stock markets and collision avoidance in multi-robot systems. The paper provides a roadmap on the progress that has been achieved in analysing the evolutionary dynamics of multi-agent learning by highlighting the main results and accomplishments.

262 citations

Journal ArticleDOI
TL;DR: The autonomy of the DelFly is expanded by achieving an improved turning logic to obtain better vision-based obstacle avoidance performance in environments with varying texture and successful onboard height control based on the pressure sensor.
Abstract: One of the major challenges in robotics is to develop a fly-like robot that can autonomously fly around in unknown environments. In this paper, we discuss the current state of the DelFly project, in which we follow a top-down approach to ever smaller and more autonomous ornithopters. The presented findings concerning the design, aerodynamics and autonomy of the DelFly illustrate some of the properties of the top-down approach, which allows the identification and resolution of issues that also play a role at smaller scales. A parametric variation of the wing stiffener layout produced a 5% more power-efficient wing. An experimental aerodynamic investigation revealed that this could be associated with an improved stiffness of the wing, while further providing evidence of the vortex development during the flap cycle. The presented experiments resulted in an improvement in the generated lift, allowing the inclusion of a yaw rate gyro, pressure sensor and microcontroller onboard the DelFly. The autonomy of the DelFly is expanded by achieving (1) an improved turning logic to obtain better vision-based obstacle avoidance performance in environments with varying texture and (2) successful onboard height control based on the pressure sensor.

188 citations

Journal ArticleDOI
TL;DR: Cunha et al. as discussed by the authors examined the 2D effective potentials for photon trajectories, and found that the emergence of stable light rings on the background spacetimes allows the formation of ''pockets'' in one of the effective potential, for open sets of impact parameters, leading to an effective trapping of some trajectories.
Abstract: In a recent paper [P. V. P. Cunha, C. A. R. Herdeiro, E. Radu, and H. F. Runarsson, Phys. Rev. Lett. 115, 211102 (2015).], it was shown that the lensing of light around rotating boson stars and Kerr black holes with scalar hair can exhibit chaotic patterns. Since no separation of variables is known (or expected) for geodesic motion on these backgrounds, we examine the 2D effective potentials for photon trajectories, to obtain a deeper understanding of this phenomenon. We find that the emergence of stable light rings on the background spacetimes allows the formation of ``pockets'' in one of the effective potentials, for open sets of impact parameters, leading to an effective trapping of some trajectories, dubbed ``quasibound orbits.'' We conclude that pocket formation induces chaotic scattering, although not all chaotic orbits are associated to pockets. These and other features are illustrated in a gallery of examples, obtained with a new ray-tracing code, pyhole, which includes tools for a simple, simultaneous visualization of the effective potential, together with the spacetime trajectory, for any given point in a lensing image. An analysis of photon orbits allows us to further establish a positive correlation between photon orbits in chaotic regions and those with more than one turning point in the radial direction; we recall that the latter is not possible around Kerr black holes. Moreover, we observe that the existence of several light rings around a horizon (several fundamental orbits, including a stable one), is a central ingredient for the existence of multiple shadows of a single hairy black hole. We also exhibit the lensing and shadows by Kerr black holes with scalar hair, observed away from the equatorial plane, obtained with pyhole.

141 citations

Journal ArticleDOI
TL;DR: A deterministic search space pruning algorithm is developed and its polynomial time and space complexity derived and the algorithm is shown to achieve search space reductions of greater than six orders of magnitude, thus reducing significantly the complexity of the subsequent optimisation.
Abstract: We introduce and describe the Multiple Gravity Assist problem, a global optimisation problem that is of great interest in the design of spacecraft and their trajectories. We discuss its formalization and we show, in one particular problem instance, the performance of selected state of the art heuristic global optimisation algorithms. A deterministic search space pruning algorithm is then developed and its polynomial time and space complexity derived. The algorithm is shown to achieve search space reductions of greater than six orders of magnitude, thus reducing significantly the complexity of the subsequent optimisation.

117 citations

Journal ArticleDOI
01 Oct 2010
TL;DR: In this paper, the authors analyzed the impact of the migration topology on the performance of a parallel global optimization algorithm using the island model, in particular parallel Differential Evolution and simulated Annealing with Adaptive Neighborhood.
Abstract: Parallel Global Optimization Algorithms (PGOA) provide an efficient way of dealing with hard optimization problems. One method of parallelization of GOAs that is frequently applied and commonly found in the contemporary literature is the so-called Island Model (IM). In this paper, we analyze the impact of the migration topology on the performance of a PGOA which uses the Island Model. In particular we consider parallel Differential Evolution and Simulated Annealing with Adaptive Neighborhood and draw first conclusions that emerge from the conducted experiments.

95 citations


Authors

Showing all 90 results

NameH-indexPapersCitations
Massimiliano Vasile364334925
Carlo Menon353355103
Aurélien Hees321142946
Sante Carloni32824645
Dario Izzo301793072
G.C.H.E. de Croon22611530
Claudio Bombardelli211011410
Camilla Pandolfi18621183
Daniel Hennes17731078
Tom Gheysens1739810
Jai Grover1630684
Leopold Summerer1449596
Luca Rossini1324889
Chit Hong Yam1324428
Francesco Biscani1223482
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20216
20208
20199
201813
20176
201614