A drive-reinforcement model of single neuron function: An alternative to the Hebbian neuronal model
AIP Conference Proceedings 151 on Neural Networks for Computing
An introduction to neural computing
An introduction to neural computing
Fuzzy engineering
Neural network design
On caching and prefetching of virtual objects in distributed virtual environments
MULTIMEDIA '98 Proceedings of the sixth ACM international conference on Multimedia
A multi-server architecture for distributed virtual walkthrough
VRST '02 Proceedings of the ACM symposium on Virtual reality software and technology
Contextual fuzzy cognitive map for decision support in geographic information systems
IEEE Transactions on Fuzzy Systems
ACM international workshop on multimedia technologies for distance learning (MTDL 2009)
MM '09 Proceedings of the 17th ACM international conference on Multimedia
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Fuzzy Cognitive Map (FCM) can be used to design game-based learning systems because it has the excellent ability of concept representation and reasoning. However, it can not (1) get new knowledge from existing data and (2) correct false transcendental knowledge by itself, which mars the game-based learning ability. This paper utilizes Hebbian learning rule to solve the first problem and uses unbalance degree to solve the second problem. As a result, the improved FCM has the ability of self-learning from both existing data and priori knowledge, and is more suitable for a game-based learning system. Based on the improved FCM, a novel game-based learning model is proposed, including a teacher submodel, a learner submodel and a set of learning mechanisms. The teacher submodel has the standard answers which can be deduced from the improved FCM. The learner submodel is bulit and adjusted according to the teacher's FCM, which reflects the learner's learning process. The learning mechanisms compute the difference between the outputs of the teacher submodel and the learner submodel, and control the whole game learning process according to the difference. Based on the proposed model, an automobile driving learning system is developed to prove the effectiveness of the proposed model. Extensive experimental results demonstrate our model validity in terms of controlling the learning process and the guiding learners learning.