The spatial semantic hierarchy
Artificial Intelligence
Robot Motion Planning
Learning domain knowledge for teaching procedural skills
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
Articulate software for science and engineering education
Smart machines in education
Using Qualitative Physics to Create Articulate Educational Software
IEEE Expert: Intelligent Systems and Their Applications
Recent Developments in Motion Planning
ICCS '02 Proceedings of the International Conference on Computational Science-Part III
The advantages of explicitly representing problem spaces
UM'03 Proceedings of the 9th international conference on User modeling
Using Knowledge Discovery Techniques to Support Tutoring in an Ill-Defined Domain
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Cognitive Tutoring System with "Consciousness"
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Implementing an efficient causal learning mechanism in a cognitive tutoring agent
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
A cognitive tutoring agent with episodic and causal learning capabilities
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
A computational model for causal learning in cognitive agents
Knowledge-Based Systems
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In this paper, we describe the open knowledge structure of Roman Tutor, a simulation-based intelligent tutoring system we are developing to teach astronauts how to manipulate the Space Station Remote Manipulator (SSRMS), known as “Canadarm II”, on the International Space Station (ISS). We show that by representing the complex ISS-related knowledge in the form of a three-layered architecture with different levels of abstraction, and by using a new approach for robot path planning called FADPRM, it is no longer necessary to plan in advance what feedback to give to the learner or to explicitly create a complex task graph to support the tutoring process.