Checking date: 14/05/2021

Course: 2021/2022

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


Requirements (Subjects that are assumed to be known)
- To present 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
1. Introduction 1.1 Knowledge representation 1.2 Heuristic Search 2. Classical planning 2.1 State space. STRIPS 2.2 Partial plans. UCPOP 3. Planning based on plan graphs 3.1 Plan graphs. GRAPHPLAN 3.2 SAT planning. SATPLAN 4. Heuristic planning 4.1 Early approaches. HSP, FF 4.2 New heuristics and planners. Fast downward, pattern data bases, landmarks, symbolic planning, portfolios 4.3 Hierarchical Task Networks (HTN). SHOP2 5. Machine learning in planning 6. Other planning paradigms 6.1 Temporal planning (scheduling) 6.2 Partial Satisfaction Planning 6.3 Planning under uncertainty
Learning activities and methodology
Lectures Weekly homework Final project with oral presentation Individual office hours
Assessment System
  • % end-of-term-examination 50
  • % of continuous assessment (assigments, laboratory, practicals...) 50
Calendar of Continuous assessment
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
Recursos electrónicosElectronic Resources *
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The course syllabus and the academic weekly planning may change due academic events or other reasons.