Inhalt

[ 536KNRRCOLV19 ] VL Computational Logics for AI

Versionsauswahl
Workload Education level Study areas Responsible person Hours per week Coordinating university
3 ECTS B2 - Bachelor's programme 2. year (*)Artificial Intelligence Martina Seidl 2 hpw Johannes Kepler University Linz
Detailed information
Original study plan Bachelor's programme Artificial Intelligence 2025W
Learning Outcomes
Competences
Students can apply classical techniques from automated deduction and are sufficiently familiar with the underlying theory that they can adapt them to reasoning techniques for related contexts.
Skills Knowledge
  • Students can recognize whether a given problem is amenable to

techniques from automated reasoning (K4).

  • Students can formulate and reason about algorithms operating on

formulas and expressions (K4).

  • Students can tell why proofs found by computational logic are

formally rigorous (K4).

  • Students can analyze a given logical calculus for 'soundness' and

'completeness' (K4).

  • Students can apply a given logical calculus to a given problem (K4).
  • Students have some understanding of the computational limitations of

computational logic (K4).

  • Syntax and semantics of propositional logic, equational logic, and

predicate logic.

  • Classical formal reasoning mechanisms for the aforementioned logics

(e.g., resolution, Knuth-Bendix completion).

  • Some ideas for managing extremely large search spaces.
Criteria for evaluation Evaluation will be based on a written final exam at the end of the semester.
Methods
  • Presentation of the content by lecturer
  • Presentation of examples
Language English
Study material
  • Slides
  • Lecture Notes
  • Recordings of the lecture
Changing subject? No
Further information This lecture and the associated exercise course form an inseparable didactic unit. The learning outcomes presented here are achieved through the close interaction of the two courses.
On-site course
Maximum number of participants -
Assignment procedure Direct assignment