Please use this identifier to cite or link to this item:
Title: Towards and Adaption and Personalisation Solution based on Multi Agent System Applied on Serious Games
Authors: Blatsios, Spyridon
Refanidis, Ioannis
Editors: Macintyre, John
Maglogiannis, Ilias
Iliadis, Lazaros
Pimenidis, Elias
Type: Conference Paper
Subjects: FRASCATI::Natural sciences::Computer and information sciences
Keywords: Adaptation
Game-based learning
Game design
Issue Date: May-2019
Publisher: Springer Nature
Volume: 559
First Page: 584
Last Page: 594
Volume Title: Artificial Intelligence Applications and Innovations. AIAI 2019
Part of Series: IFIP International Conference on Artificial Intelligence Applications and Innovations
Part of Series: IFIP International Conference on Artificial Intelligence Applications and Innovations
Abstract: Serious games (SG) have the potential to become one of the most important future e-learning tools. The use of SG in education is a large deviation from the common education standards, which usually are based on mass systems of instruction, assessment, grading and reporting students’ knowledge and skills. SG encourage self‐directness and independency of student, thus providing a framework for self-learning activities. However, the benefits of using SG as a learning tool are maximized in a personalised and adaptive environment. Although it has been suggested in the past that SG can take advantage of Artificial Intelligence (AI) methods for automated adaptation to the learner, there is not so much research in the field. Taking the above into consideration, this paper aims to provide a framework on adaptive and personalised SG using AI methods. The advances in technology have made it possible to trace and collect user generated data that we can use to capture essentially players’ in-game behaviours and trace knowledge or skills acquired from the player during playing. This will actually be a two-step process, “User Identification” and “Content Adaptation” to learners’ needs. In he proposed methodology “User Identification” will be implemented from data derived from “User Behaviour” and “System Feedback”. That data will feed a Learner Agent supported by an Adaption and Personalisation engine, which will interact with both the “Instructional Content” and “Game Characteristics” in order to achieve the desired adaption. This paper will be used as a basis for further development of an adaptive and personalised SG.
ISBN: 978-3-030-19822-0
Electronic ISBN: 978-3-030-19823-7
ISSN: 1868-4238
Electronic ISSN: 1868-422X
Other Identifiers: 10.1007/978-3-030-19823-7_49
Appears in Collections:Department of Applied Informatics

Files in This Item:
File Description SizeFormat 
Blatsios_AAAI2019 - preprint.pdfpreprint279,13 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.