CS 461 Artificial Intelligence

Semantic nets and description matching. Generate and test, Means-ends analysis, and problem reduction. Nets and basic search. Nets and optimal search. Trees and adversarial search. Rules and rule chaining. Rules, substrates, and cognitive modeling. Frames and inheritance. Frames and commonsense. Numeric constraints and propagation. Symbolic constraints and propagation. Logic and resolution proof. Backtracking and truth maintenance. Planning. Learning by analyzing differences. Learning by explaining experience. Learning by correcting mistakes. Learning by recording cases. Learning by managing multiple models. Learning by building identification trees. Learning by training neural nets. Learning by training perceptrons. Learning by training approximation nets. Learning by simulating evolution. Recognizing objects. Describing images. Expressing language constraints. Responding to questions and commands. Credit units: 3 ECTS Credit units: 6.

Autumn Semester (Varol Akman)

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