Inhalt

[ 993TASMPRAV22 ] VL Planning and Reasoning in Artificial Intelligence

Versionsauswahl
Workload Education level Study areas Responsible person Hours per week Coordinating university
3 ECTS M1 - Master's programme 1. year (*)Artificial Intelligence Martina Seidl 2 hpw Johannes Kepler University Linz
Detailed information
Original study plan Master's programme Artificial Intelligence 2025W
Learning Outcomes
Competences
The students can analyze the strengths and limitations of various reasoning formalisms and from a pool of different formalisms, they can select a suitable formalism for efficiently encoding a specific reasoning task. They can employ reasoning tools to evaluate the encoding and they can interpret the results of the reasoning tools and map them back to the original reasoning task.
Skills Knowledge
  • understand multiple formalisms for symbolic reasoning (K2, K3)
  • employ symbolic reasoners for problem solving (K2, K3, K4)
  • formulate symbolic encodings (K4, K5, K6)
  • assess if a formalism is suitable for a specific application problem (K4)
Students acquire knowledge of symbolic reasoning techniques and their underlying theory, focusing on their application in domains such as planning and formal verification. They learn how to encode application problems into formal systems and analyze the effectiveness of inference mechanisms, gaining insights into the advantages and limitations of symbolic reasoning in AI.
Criteria for evaluation The evaluation is together with the exercise class and is based on:

  • Weekly online exercises
  • Projects in teams and presentations of the solutions
  • Short presentation of a recent topic related to the course

Alternatively, there is also an exam offered.

Methods
  • Weekly lecture
  • Weekly exercises
  • Two encoding projects
  • Literature study/tool evaluation
Language English
Study material
  • Slides of the lecture
  • Recordings of the lectures
  • Links to recent literature
  • Solutions to some of the exercises
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.
Corresponding lecture 201COMACALV18: VL Computer Algebra (3 ECTS)
Earlier variants They also cover the requirements of the curriculum (from - to)
993TARKPRAV20: VL Planning and Reasoning in Artificial Intelligence (2020W-2022S)
On-site course
Maximum number of participants -
Assignment procedure Direct assignment