Checking date: 09/07/2020

Course: 2020/2021

Automated Planning
Study: Master in Computer Science and Technology (71)

Coordinating teacher: GARCIA OLAYA, ANGEL

Department assigned to the subject: Department of Computer Science and Engineering

Type: Electives
ECTS Credits: 3.0 ECTS


Students are expected to have completed
Competences and skills that will be acquired and learning results.
- To analyze state-of-the-art automated planning techniques - To characterize every technique as well as the domains they suit better - To use tools that implement techniques discussed in class - To identify different open issues for research in order to suggest new Master and PhD thesis
Description of contents: programme
Introduction Introduction to planning Knowledge representation Heuristic Search Classic planning State space. STRIPS and Prodigy Partial plans. UCPOP Neoclassic planning Plan graphs. GRAPHPLAN SAT planning. SATPLAN Heuristic planning Early approaches. HSP, FF New heuristics and planners. Fast downward, pattern data bases, landmarks, symbolic planning, portfolios Hierarchical Task Networks (HTN). SHOP2 Machine learning Other planning paradigms Temporal planning (scheduling) Planning under uncertainty
Learning activities and methodology
Theory classes One homework per two weeks Final project Oral presentation of project Individual office hours
Assessment System
  • % end-of-term-examination 50
  • % of continuous assessment (assigments, laboratory, practicals...) 50
Basic Bibliography
  • James F. Allen, James Hendler y Austin Tate (eds.). Readings in planning. Morgan Kaufmann, 1990..
  • Malik Ghallab, Dana Nau, Paolo Traverso. Automated Task Planning. Theory & Practice. Morgan Kaufmann, 2004.
  • Stuart Russell y Peter Norvig. Artificial Intelligence: A modern approach. Prentice Hall. 2010

The course syllabus and the academic weekly planning may change due academic events or other reasons.