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      <p>CRoNe, the Congress on Robotics and Neuroscience, is an encounter
that acts not only as a bridge but also as a fruitful land for
collaboration and discussion concerning recent advances in the frontiers of
artificial intelligence, robotics and neuroscience, fostering the exchange
of ideas among different, and often fairly separated, scientific fields.
The congress, part of the Latin American Robotics Week1, organized
by Innovación y Robótica Estudiantil UTFSM 2, is a meeting point for
people from engineering, human and biological sciences promoting
the development and understanding of complex intelligent systems.</p>
      <p>At its 4th version, the Congress on Robotics and Neuroscience was
focused on four different areas:
- Development of meaning: with approaches from Developmental</p>
      <p>Robotics, Machine Learning, and Brain-based theories.
- Predictive coding for cognitive development: introducing analyses
from Neuroscience, Computational Neuroscience, and AI.
- Multimodal cognition: under neuroscientific and psychological
scopes.
- Experimental analyses and methodologies: presenting novel
methodologies for closed-loop brain training, brain functional
connectivity analyses and Machine Learning applications.
On this opportunity, we had the pleasure to count with ten keynote
speakers who presented state-of-the-art results encouraging
discussions in relation to one or more of these areas. Three workshops
introducing scientific tools and methodologies completed the program,
spreading knowledge and access to processing and developmental
environments on Interactive Reinforcement Learning and data
analysis in Electroencephalogram signals.</p>
      <p>
        The accepted works presented during the conference, collected
in this volume, tackles a wide variaty of problems comming from
areas as robotics, education or data analytics. Along the different
proposed analyses, while some approaches are based on a single
domain, discussions as the ones in
        <xref ref-type="bibr" rid="ref6">Torres (2018)</xref>
        are crossed among
areas.
        <xref ref-type="bibr" rid="ref6">Torres (2018)</xref>
        evaluates different strategies for finding trending
topics on a set of scientific articles, through a discussion arround the
1 Further details in
      </p>
      <p>http://www.roboticsweekla.com.
2 a multidisciplinary group of both,
undergraduate and graduate students,
focused on R&amp;D with emphasis on
robotics. More information in
http://innovacionyrobotica.usm.cl.
recent approaches aiming to describe the brain processes of memory.</p>
      <p>
        Following context characteristics,
        <xref ref-type="bibr" rid="ref4">Ollino et al. (2018)</xref>
        propose a
single-agent approach to tackle a multi-agent problem using batch
reinforcement learning for developing defensive strategies in a
RoboCup SSL (Small Size League) robotic team. The proposed
implementation takes advantage of the framework of the team controller,
which usually has a team-level decision-maker. The controller learns
and executes different responses detailing the action for each agent,
from a pre-defined set of actions, following a single team goal to
minimize the score of an enemy team. In this same scenario, the already
competing team shown in
        <xref ref-type="bibr" rid="ref2">Aubel et al. (2018)</xref>
        , presents an in-depth
description about design and implementation tasks for different areas
tackled in a real SSL team (with physical prototypes), with
challenges ranging from structural issues to field strategy problems. In
another real world scenario,
        <xref ref-type="bibr" rid="ref5">Silva et al. (2018)</xref>
        describes CRABOT, a
six-legged robotic platform for autonomous field recognition and
object manipulation. CRABOT was developed under a leg-arm hybrid
design: similar to a crab, it can stand on four of its legs leaving two
completely free for object interaction, being able to deploy different
tools attached to each limb.
      </p>
      <p>
        Regarding to educational approaches, work in
        <xref ref-type="bibr" rid="ref7">Vallejo-Jiménez
et al. (2018)</xref>
        describes some experiences from environments
defined as technological academies for STEM
(Science-TechnologyEngineering-Math) learning scenarios, for primary and secondary
level school students in courses such as Mathematics, Physics or
applied sciences such as Robotics or Virtual Technologies. In a more
theoretical approach, work in
        <xref ref-type="bibr" rid="ref1">Angel and Nettle (2018)</xref>
        presents a
review over the last decades of educative methodologies,
identifying keys for the development of competences and the acquisition
of meaningful learning. The proposal from
        <xref ref-type="bibr" rid="ref1">Angel and Nettle (2018)</xref>
        conclude with a procedural model for educational scenarios, which
incorporates metacognitive practices for achieving student’s
awareness of what is learnt and how and when to apply it.
      </p>
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