Implementing an efficient causal learning mechanism in a cognitive tutoring agent

Authors:
Usef Faghihi;Philippe Fournier-Viger;Roger Nkambou
Affiliations:
Department of Computer Science, University of Memphis, Tennessee;Department of Computer Science and Information Engineering, National Cheng Kung University, Taiwan;Department of Computer Science, University of Quebec in Montreal, Montreal, Canada
Venue:
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
Year:
2011

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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.