Abstract
An important research problem for developing complex cognitive agents is to provide them with human-like learning mechanisms. One important type of learning among episodic, emotional, and procedural learning is causal learning. In current cognitive agents, causal learning has up to now been implemented with techniques such as Bayesian networks that are not scalable for handling a large volume of data like a human does. In this paper, we address this problem of causal learning using a modified constraint based data mining algorithm that respects temporal ordering of the events. That is, from a huge amount of data, the algorithm filters the data to extract the most important information. We illustrate the application of this learning mechanism in a cognitive tutoring agent for the complex domain of teaching robotic arm manipulation.
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Faghihi, U., Fournier-viger, P., Nkambou, R. (2011). Implementing an Efficient Causal Learning Mechanism in a Cognitive Tutoring Agent. In: Mehrotra, K.G., Mohan, C.K., Oh, J.C., Varshney, P.K., Ali, M. (eds) Modern Approaches in Applied Intelligence. IEA/AIE 2011. Lecture Notes in Computer Science(), vol 6704. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21827-9_4
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DOI: https://doi.org/10.1007/978-3-642-21827-9_4
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