Improvement of action selection in robots based on learning fuzzy cognitive map and analysis of variance: the case of soccer server simulation environment

Seyed Koosha Golmohammadi, Ali Azadeh, Amir Hossein Gharehgozli and Zeinab Raoofi

International Journal of Industrial and Systems Engineering, 2014, vol. 16, issue 2, 184-199

Abstract: One of the main issues in developing automatic response systems especially autonomous robots is selecting the best action among all possible actions. Fuzzy cognitive maps (FCMs) aim to mimic the reasoning process of the human. FCMs are able to capture and imitate human behaviour by describing, developing and representing models. FCMs are also popular for their simplicity and transparency while being successful in a variety of applications. We developed a novel model that could be used for action selection in robots. This model is constructed on a learning FCM which is relied on improved non-linear Hebbian algorithm. We tested our model through a series of practical experiments on the latest version of Soccer Server Simulation 3D environment. Our tests involved carefully defined factors to measure the team performance. Our results showed a significant improvement in overall performance. The significance of the proposed model was verified by analysis of variance (ANOVA).

Keywords: fuzzy cognitive maps; FCM; learning improvement; robot action selection; soccer server simulation; analysis of variance; ANOVA; automatic response systems; autonomous robots; human reasoning; human behaviour; modelling; robot actions; team performance; robot learning. (search for similar items in EconPapers)
Date: 2014
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=58835 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijisen:v:16:y:2014:i:2:p:184-199

Access Statistics for this article

More articles in International Journal of Industrial and Systems Engineering from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().