Artificial Intelligence - Special volume on qualitative reasoning about physical systems
SOAR: an architecture for general intelligence
Artificial Intelligence
Compositional modeling: finding the right model for the job
Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
Propositional knowledge base revision and minimal change
Artificial Intelligence
Automated modeling of complex systems to answer prediction questions
Artificial Intelligence
Investigating Explanation-Based Learning
Investigating Explanation-Based Learning
Repairing Learned Knowledge Using Experience
Repairing Learned Knowledge Using Experience
Companion Cognitive Systems: Design Goals and Lessons Learned So Far
IEEE Intelligent Systems
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Learning concepts via instruction and expository texts is an important problem for modeling human learning and for making autonomous AI systems. This paper describes a computational model of the self-explanation effect, whereby conceptual knowledge is repaired by integrating and explaining new material. Our model represents conceptual knowledge with compositional model fragments, which are used to explain new material via model formulation. Preferences are computed over explanations and conceptual knowledge, along several dimensions. These preferences guide knowledge integration and question-answering. Our simulation learns about the human circulatory system, using facts from a circulatory system passage used in a previous cognitive psychology experiment. We analyze the simulation's performance, showing that individual differences in sequences of models learned by students can be explained by different parameter settings in our model.