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Swarm Intelligence: 12th International Conference, ANTS 2020, Barcelona, Spain, October 26–28, 2020, Proceedings
AutoMoDe-Arlequin: Neural Networks as Behavioral Modules for the Automatic Design of Probabilistic Finite-State Machines
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Author(s):
Antoine Ligot
,
Ken Hasselmann
,
Mauro Birattari
Publication date
(Online):
October 23 2020
Publisher:
Springer International Publishing
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Neural Networks and the Bias/Variance Dilemma
Stuart Geman
,
Elie Bienenstock
,
René Doursat
(1992)
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Swarm robotics: a review from the swarm engineering perspective
Manuele Brambilla
,
Eliseo Ferrante
,
Mauro Birattari
…
(2013)
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The irace package: Iterated racing for automatic algorithm configuration
Manuel López-Ibáñez
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Jérémie Dubois-Lacoste
,
Leslie Pérez Cáceres
…
(2016)
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Contributors
Antoine Ligot:
(View ORCID Profile)
Ken Hasselmann:
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Mauro Birattari:
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Book Chapter
Publication date (Print):
2020
Publication date (Online):
October 23 2020
Pages
: 271-281
DOI:
10.1007/978-3-030-60376-2_21
SO-VID:
1242b811-acdb-4d2d-99d4-ea5548ed9d52
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Book chapters
pp. 3
A Blockchain-Controlled Physical Robot Swarm Communicating via an Ad-Hoc Network
pp. 16
A New Approach for Making Use of Negative Learning in Ant Colony Optimization
pp. 29
Ant Colony Optimization for Object-Oriented Unit Test Generation
pp. 42
Branched Structure Formation in a Decentralized Flock of Wheeled Robots
pp. 55
Collective Decision Making in Swarm Robotics with Distributed Bayesian Hypothesis Testing
pp. 68
Constrained Scheduling of Step-Controlled Buffering Energy Resources with Ant Colony Optimization
pp. 82
Construction Task Allocation Through the Collective Perception of a Dynamic Environment
pp. 107
Dynamic Response Thresholds: Heterogeneous Ranges Allow Specialization While Mitigating Convergence to Sink States
pp. 121
Grey Wolf, Firefly and Bat Algorithms: Three Widespread Algorithms that Do Not Contain Any Novelty
pp. 148
Heterogeneous Response Intensity Ranges and Response Probability Improve Goal Achievement in Multi-agent Systems
pp. 176
Memory Induced Aggregation in Collective Foraging
pp. 190
Modeling Pathfinding for Swarm Robotics
pp. 203
Motion Dynamics of Foragers in Honey Bee Colonies
pp. 216
Multi-robot Coverage Using Self-organized Networks for Central Coordination
pp. 229
Robot Distancing: Planar Construction with Lanes
pp. 243
The Pi-puck Ecosystem: Hardware and Software Support for the e-puck and e-puck2
pp. 256
Zealots Attack and the Revenge of the Commons: Quality vs Quantity in the Best-of-n
pp. 271
AutoMoDe-Arlequin: Neural Networks as Behavioral Modules for the Automatic Design of Probabilistic Finite-State Machines
pp. 282
Coalition Formation Problem: A Group Dynamics Inspired Swarming Method
pp. 290
Collective Gradient Perception in a Flocking Robot Swarm
pp. 298
Fitting Gaussian Mixture Models Using Cooperative Particle Swarm Optimization
pp. 315
Group-Size Regulation in Self-organized Aggregation in Robot Swarms
pp. 324
On the Effects of Minimally Invasive Collision Avoidance on an Emergent Behavior
pp. 333
Set-Based Particle Swarm Optimization for Portfolio Optimization
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