Logic and Mechanized Reasoning
Symbolic logic is fundamental to computer science, providing a foundation
for the theory of programming languages, the theory of databases, AI,
knowledge representation, automated reasoning, and formal verification.
Formal methods based on logic complement statistical methods and machine
learning by providing rules of inference and means of representation with
precise semantics. These methods are central to hardware and software
verification, and have also been used to solve open problems in mathematics.
This course is an introduction to symbolic logic on three levels: theory, implementation, and application. We will present the underlying mathematical theory, and students will develop the mathematical skills that are needed to design and reason about logical systems in a rigorous way. We will also show students how to represent logical objects in a functional programming language, Lean, and how to implement fundamental logical algorithms. Finally, we will show students how to use contemporary automated reasoning tools, including SAT solvers, SMT solvers, and first-order theorem prover, to solve challenging problems, and we will show students how to use Lean as an interactive theorem prover.
Key Topics: mathematical foundations, propositional logic, first-order logic, fundamental algorithms, SAT solvers, SMT solvers, and first-order theorem provers.
Prerequisite knowledge: None except prerequisite courses 15-151 and 15-150.
- Location: Gates Hillman Complex 9107
- Email: email@example.com
- Office Hours: Thursdays from 3-4pm (in person).
- Location: Baker Hall 135E
- Email: firstname.lastname@example.org
- Office Hours: Wednesdays from 3-4pm (Zoom).
Teaching assistant:Wojciech Nawrocki
- Location: Doherty Hall 4301A
- Email: email@example.com
- Office Hours: Tuesdays and Wednesdays from 2-3pm (in person).
Lectures: Tuesdays and Thursdays from 11:50am to 1:10pm in Porter 125D
Textbook: https://avigad.github.io/lamr/ (work in progress)
Grading: Grades are based on homework assignments (40%) and on the three exams (60% = 20% × 3).
Late policy: Homework assignments that are submitted late will be penalized by subtracking 5 points per day (10%).
Learning Resources: The course will provide an online interactive textbook and the relevant software.
Academic Integrity: Students are expected to complete each homework assignment on their own, and should be able to explain all of the work that they hand in. Copying material from other students or online sources is not allowed. If you have questions about whether something might be an issue, contact the course staff before discussing further. Please refer to the Carnegie Mellon Code for information about university policies regarding academic conduct.
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