15 Result(s)
within
Nicolas Bredeche
Computer Science
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Chapter and Conference Paper
Cooperative Co-evolution and Adaptive Team Composition for a Multi-rover Resource Allocation Problem
In this paper, we are interested in ad hoc autonomous agent team composition using cooperative co-evolutionary algorithms (CCEA). In order to accurately capture the individual contribution of team agents, we p...
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Chapter and Conference Paper
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 co...
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Chapter and Conference Paper
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 th...
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Chapter and Conference Paper
How to Blend a Robot Within a Group of Zebrafish: Achieving Social Acceptance Through Real-Time Calibration of a Multi-level Behavioural Model
We have previously shown how to socially integrate a fish robot into a group of zebrafish thanks to biomimetic behavioural models. The models have to be calibrated on experimental data to present correct behav...
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Chapter and Conference Paper
Automated Calibration of a Biomimetic Space-Dependent Model for Zebrafish and Robot Collective Behaviour in a Structured Environment
Bio-hybrid systems made of robots and animals can be useful tools both for biology and robotics. To socially integrate robots into animal groups the robots should behave in a biomimetic manner with close loop ...
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Chapter and Conference Paper
Tutorials at PPSN 2016
PPSN 2016 hosts a total number of 16 tutorials covering a broad range of current research in evolutionary computation. The tutorials range from introductory to advanced and specialized but can all be attended ...
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Chapter and Conference Paper
Multi-objective Optimization of Multi-level Models for Controlling Animal Collective Behavior with Robots
Group-living animals often exhibit complex collective behaviors that emerge through the non-linear dynamics of social interactions between individuals. Previous studies have shown that it is possible to influe...
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Chapter and Conference Paper
Environment-Driven Embodied Evolution in a Population of Autonomous Agents
This paper is concerned with a fixed-size population of autonomous agents facing unknown, possibly changing, environments. The motivation is to design an embodied evolutionary algorithm that can cope with the ...
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Chapter and Conference Paper
A Statistical Learning Perspective of Genetic Programming
This paper proposes a theoretical analysis of Genetic Programming (GP) from the perspective of statistical learning theory, a well grounded mathematical toolbox for machine learning. By computing the Vapnik-Ch...
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Chapter and Conference Paper
Unsupervised Learning of Echo State Networks: A Case Study in Artificial Embryogeny
Echo State Networks (ESN) have demonstrated their efficiency in supervised learning of time series: a ”reservoir” of neurons provide a set of dynamical systems that can be linearly combined to match the target...
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Chapter and Conference Paper
Simbad: An Autonomous Robot Simulation Package for Education and Research
Simbad is an open source Java 3d robot simulator for scientific and educational purposes. It is mainly dedicated to researchers and programmers who want a simple basis for studying Situated Artificial Intellig...
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Chapter and Conference Paper
Blindbuilder: A New Encoding to Evolve Lego-Like Structures
This paper introduces a new representation for assemblies of small Lego®-like elements: structures are indirectly encoded as construction plans. This representation shows some interesting properties such as hiera...
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Chapter and Conference Paper
From Factorial and Hierarchical HMM to Bayesian Network: A Representation Change Algorithm
Factorial Hierarchical Hidden Markov Models (FHHMM) provides a powerful way to endow an autonomous mobile robot with efficient map-building and map-navigation behaviors. However, the inference mechanism in FHH...
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Chapter and Conference Paper
A Wrapper-Based Approach to Robot Learning Concepts from Images
This work is about the building of a lexicon of shared symbols between a Pioneer2DX mobile robot and its human interlocutors. This lexicon contains words corresponding to objects seen in the environment. The diff...
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Chapter and Conference Paper
Abstracting Visual Percepts to Learn Concepts
To efficiently identify properties from its environment is an essential ability of a mobile robot who needs to interact with humans. Successful approaches to provide robots with such ability are based on ad-ho...
