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Fear Learning for Flexible Decision Making in RoboCup: A Discussion

Rizzi, Caroline and Johnson, Colin G. and Vargas, Patricia (2018) Fear Learning for Flexible Decision Making in RoboCup: A Discussion. In: Akiyama, Hidehisa and Obst, Oliver and Sammut, Claude and Tonidandel, Flavio, eds. RoboCup 2017: Robot World Cup XXI. Lecture Notes in Artificial Intelligence, 11175 . Springer, Germany, pp. 59-70. ISBN 978-3-030-00307-4. E-ISBN 978-3-030-00308-1. (doi:10.1007/978-3-030-00308-1_5)

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https://doi.org/10.1007/978-3-030-00308-1_5

Abstract

In this paper, we address the stagnation of RoboCup com- petitions 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.

Item Type: Book section
DOI/Identification number: 10.1007/978-3-030-00308-1_5
Uncontrolled keywords: RoboCup, Cognitive learning, Contextual fear conditioning, Brain emotional model, Affective computing
Subjects: Q Science > QA Mathematics (inc Computing science)
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.87 Neural computers, neural networks
Divisions: Faculties > Sciences > School of Computing
Depositing User: Colin Johnson
Date Deposited: 17 Oct 2018 10:17 UTC
Last Modified: 29 May 2019 21:18 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/69611 (The current URI for this page, for reference purposes)
Johnson, Colin G.: https://orcid.org/0000-0002-9236-6581
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