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Gianluca Baldassarre
Researcher at National Research Council
Publications - 183
Citations - 5672
Gianluca Baldassarre is an academic researcher from National Research Council. The author has contributed to research in topics: Reinforcement learning & Computer science. The author has an hindex of 38, co-authored 157 publications receiving 4796 citations. Previous affiliations of Gianluca Baldassarre include South Carolina State University & University of Essex.
Papers
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Journal ArticleDOI
Evolving Self-Organizing Behaviors for a Swarm-Bot
Marco Dorigo,Vito Trianni,Erol Sahin,Roderich Groß,Thomas Halva Labella,Gianluca Baldassarre,Stefano Nolfi,Jean-Louis Deneubourg,Francesco Mondada,Dario Floreano,Luca Maria Gambardella +10 more
TL;DR: This paper introduces a self-assembling and self-organizing artifact, called a swarm-bot, composed of a swarm of s-bots, mobile robots with the ability to connect to and to disconnect from each other.
Journal ArticleDOI
Consensus Paper: Towards a Systems-Level View of Cerebellar Function: the Interplay Between Cerebellum, Basal Ganglia, and Cortex.
Daniele Caligiore,Giovanni Pezzulo,Gianluca Baldassarre,Andreea C. Bostan,Peter L. Strick,Kenji Doya,Rick C. Helmich,Michiel F. Dirkx,James C. Houk,Henrik Jörntell,Ángel Lago-Rodríguez,Joseph M. Galea,R. Chris Miall,Traian Popa,Asha Kishore,Paul F. M. J. Verschure,Riccardo Zucca,Ivan Herreros +17 more
TL;DR: There is unanimous consensus between the authors that future experimental and computational work is needed to understand the function of cerebellar-basal ganglia circuitry in both motor and non-motor functions.
Journal ArticleDOI
Novelty or surprise
TL;DR: It is argued that opportunities for improved understanding of behavior and its neural basis are likely being missed by failing to distinguish between novelty and surprise.
BookDOI
Intrinsically Motivated Learning in Natural and Artificial Systems
TL;DR: This book introduces the concept of intrinsic motivation in artificial systems, reviews the relevant literature, offers insights from the neural and behavioural sciences, and presents novel tools for research.
Journal ArticleDOI
Evolving mobile robots able to display collective behaviors
TL;DR: The results presented in the article demonstrate that evolutionary techniques, by exploiting the self-organizing behavioral properties that emerge from the interactions between the robots and between the Robots and the environment, are a powerful method for synthesizing collective behavior.