Fear learning for flexible decision making in robocup: A discussion

Caroline Rizzi*, Colin G. Johnson, Patricia A. Vargas

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)
67 Downloads (Pure)


In this paper, we address the stagnation of RoboCup competitions in the fields of contextual perception, real-time adaptation and flexible decision-making, mainly in regards to the Standard Platform League (SPL). We argue that our Situation-Aware FEar Learning (SAFEL) model has the necessary tools to leverage the SPL competition in these fields of research, by allowing robot players to learn the behaviour profile of the opponent team at runtime. Later, players can use this knowledge to predict when an undesirable outcome is imminent, thus having the chance to act towards preventing it. We discuss specific scenarios where SAFEL’s associative learning could help to increase the positive outcomes of a team during a soccer match by means of contextual adaptation.

Original languageEnglish
Title of host publicationRoboCup 2017
Subtitle of host publicationRobot World Cup XXI
EditorsHidehisa Akiyama, Oliver Obst, Claude Sammut, Flavio Tonidandel
Number of pages12
ISBN (Electronic)9783030003081
ISBN (Print)9783030003074
Publication statusPublished - 7 Sept 2018
Event21st RoboCup International Symposium 2017 - Nagoya, Japan
Duration: 27 Jul 201731 Jul 2017

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference21st RoboCup International Symposium 2017


  • Affective computing
  • Brain emotional model
  • Cognitive learning
  • Contextual fear conditioning
  • RoboCup

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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