Publication
Machine Learning algorithms applied to the classification of robotic soccer formations and opponent teams
| dc.contributor.author | Faria, Brígida Mónica | |
| dc.contributor.author | Reis, Luís Paulo | |
| dc.contributor.author | Lau, Nuno | |
| dc.contributor.author | Castillo, Gladys | |
| dc.date.accessioned | 2024-05-03T09:18:24Z | |
| dc.date.available | 2024-05-03T09:18:24Z | |
| dc.date.issued | 2010-07-23 | |
| dc.description.abstract | Machine Learning (ML) and Knowledge Discovery (KD) are research areas with several different applications but that share a common objective of acquiring more and new information from data. This paper presents an application of several ML techniques in the identification of the opponent team and also on the classification of robotic soccer formations in the context of RoboCup international robotic soccer competition. RoboCup international project includes several distinct leagues were teams composed by different types of real or simulated robots play soccer games following a set of pre-established rules. The simulated 2D league uses simulated robots encouraging research on artificial intelligence methodologies like high-level coordination and machine learning techniques. The experimental tests performed, using four distinct datasets, enabled us to conclude that the Support Vector Machines (SVM) technique has higher accuracy than the k-Nearest Neighbor, Neural Networks and Kernel Naïve Bayes in terms of adaptation to a new kind of data. Also, the experimental results enable to conclude that using the Principal Component Analysis SVM achieves worse results than using simpler methods that have as primary assumption the distance between samples, like k-NN. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Faria, B. M., Reis, L. P., Lau, N., & Castillo, G. (2010). Machine Learning algorithms applied to the classification of robotic soccer formations and opponent teams. 2010 IEEE Conference on Cybernetics and Intelligent Systems, 344–349. https://doi.org/10.1109/ICCIS.2010.5518540 | pt_PT |
| dc.identifier.doi | 10.1109/ICCIS.2010.5518540 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.22/25453 | |
| dc.language.iso | eng | pt_PT |
| dc.publisher | IEEE Xplore | pt_PT |
| dc.relation | The authors would like to acknowledge to FCT – Portuguese Science and Technology Foundation (PhD Scholarship FCT/ SFRH / BD / 44541 /2008), DETI/UA – Dep. Electrónica Telecomunicações e Informática and ESTSP/IPP – Esc. Sup. Tecnologia Saúde Porto-IPP. This work was partially supported by project FCT-PTDC/EIA170695/2006 – “ACORD: Adaptative Coordination of Robot Teams”. | pt_PT |
| dc.relation.publisherversion | https://ieeexplore.ieee.org/abstract/document/5518540 | pt_PT |
| dc.subject | Machine learning | pt_PT |
| dc.subject | Principal component analysis | pt_PT |
| dc.subject | Support vector machines | pt_PT |
| dc.subject | RoboCup | pt_PT |
| dc.subject | Soccer simulation | pt_PT |
| dc.title | Machine Learning algorithms applied to the classification of robotic soccer formations and opponent teams | pt_PT |
| dc.type | conference object | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 349 | pt_PT |
| oaire.citation.startPage | 334 | pt_PT |
| oaire.citation.title | 2010 IEEE Conference on Cybernetics and Intelligent Systems | pt_PT |
| person.familyName | Faria | |
| person.givenName | Brigida Monica | |
| person.identifier | R-000-T1F | |
| person.identifier.ciencia-id | 0D1F-FB5E-55E4 | |
| person.identifier.orcid | 0000-0003-2102-3407 | |
| person.identifier.rid | C-6649-2012 | |
| person.identifier.scopus-author-id | 6506476517 | |
| rcaap.rights | closedAccess | pt_PT |
| rcaap.type | conferenceObject | pt_PT |
| relation.isAuthorOfPublication | 85832a40-7ef9-431a-be0c-78b45ebbae86 | |
| relation.isAuthorOfPublication.latestForDiscovery | 85832a40-7ef9-431a-be0c-78b45ebbae86 |