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To mimic human tutors and provide optimal training, a cognitive tutoring agent should be able to continuously learn from its interactions with learners.
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Third, two surveys (a pre-survey and post-survey) collected student beliefs about mathematics and reactions to the tutoring agents.
If any tutor finishes learning, its identity is stored in the inactive list (lines 30 33) and its worker agents are reassigned to tutor agents that are still active.
Each dialog included statements by both a tutor agent and a student agent.
Each chaser hunter inherits the problem space and the Q-table of its tutor agent.
Each blocker hunter inherits the problem space and the Q-table of its tutor agent.
Each tutor agent aggregates its workers' Q-tables into its own Q-table.
These chaser agents inherit the problem space of the tutor agent of the chosen sub-grid (Line 5).
In a trialog, the tutor agent asked a main question and both the human student and student agent would respond with answers, then the tutor agent would provide feedback and follow-up questions that scaffold the explanation.
The QA-learning algorithm aggregates the Q-tables of worker agents into a central repository managed by their tutor agent.
The more advanced student can attempt to teach the peer agent, with the tutor agent stepping in as needed.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com