TY - GEN
T1 - CAMEG - A multi-agent based context-aware mobile educational game for on-the-job training
AU - Lu, Chris
AU - Chang, Maiga
AU - Kinshuk,
AU - Huang, Echo
AU - Chen, Ching Wen
PY - 2010
Y1 - 2010
N2 - In this paper, we present a multi-agent based context-aware mobile educational game that can generate a series of learning activities for users doing on-the-job training in their working environment. We apply multi-agent architecture (MAA) into the mobile educational game design to achieve the goals of developing a lightweight, flexible, and scalable game on the platform with limited resources such as mobile phones. Multi-agent architecture not only makes different agents have its own tasks, but also provides developers an expandable way to add further functions into the game and to polish agents in order to make improvement on the game. This research focuses on designing the tasks that each agent needs to do and the communications may happen among agents. The benefits of the proposed multi-agent architecture game design makes the game itself easy to maintain and to expand, at meanwhile, reduces computing power consumed by the systems due to not all agents will be needed at same time.
AB - In this paper, we present a multi-agent based context-aware mobile educational game that can generate a series of learning activities for users doing on-the-job training in their working environment. We apply multi-agent architecture (MAA) into the mobile educational game design to achieve the goals of developing a lightweight, flexible, and scalable game on the platform with limited resources such as mobile phones. Multi-agent architecture not only makes different agents have its own tasks, but also provides developers an expandable way to add further functions into the game and to polish agents in order to make improvement on the game. This research focuses on designing the tasks that each agent needs to do and the communications may happen among agents. The benefits of the proposed multi-agent architecture game design makes the game itself easy to maintain and to expand, at meanwhile, reduces computing power consumed by the systems due to not all agents will be needed at same time.
KW - Context-aware, knowledge structure
KW - Game-based learning
KW - Mobile phone
KW - On-the-job training, multi-agent system, situated learning
UR - http://www.scopus.com/inward/record.url?scp=84864556693&partnerID=8YFLogxK
M3 - Published Conference contribution
AN - SCOPUS:84864556693
SN - 9789834251246
T3 - Workshop Proceedings of the 18th International Conference on Computers in Education, ICCE 2010
SP - 341
EP - 345
BT - Workshop Proceedings of the 18th International Conference on Computers in Education, ICCE 2010
T2 - 18th International Conference on Computers in Education, ICCE 2010
Y2 - 29 November 2010 through 3 December 2010
ER -