@inproceedings{4f080e4e3c364715a6779a7e47e54a35,
title = "Fear learning for flexible decision making in robocup: A discussion",
abstract = "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{\textquoteright}s associative learning could help to increase the positive outcomes of a team during a soccer match by means of contextual adaptation.",
keywords = "Affective computing, Brain emotional model, Cognitive learning, Contextual fear conditioning, RoboCup",
author = "Caroline Rizzi and Johnson, {Colin G.} and Vargas, {Patricia A.}",
year = "2018",
month = sep,
day = "7",
doi = "10.1007/978-3-030-00308-1_5",
language = "English",
isbn = "9783030003074",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "59--70",
editor = "Hidehisa Akiyama and Oliver Obst and Claude Sammut and Flavio Tonidandel",
booktitle = "RoboCup 2017",
note = "21st RoboCup International Symposium 2017 ; Conference date: 27-07-2017 Through 31-07-2017",
}