Checking date: 14/07/2021


Course: 2021/2022

Automated Planning
(18657)
Study: Master in Computer Engineering (228)
EPI


Coordinating teacher: GARCIA OLAYA, ANGEL

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

Type: Electives
ECTS Credits: 3.0 ECTS

Course:
Semester:




Requirements (Subjects that are assumed to be known)
None
Objectives
- 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
Skills and learning outcomes
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
LEARNING ACTIVITIES AF3: Theoretical-practical classes [23.33 hours, 100% face-to-face, 0.78 ECTS] AF5 - Tutorials [8 hours, 25% face-to-face, 0.27 ECTS] AF7 - Individual student work [55.67 hours, 0% face-to-face, 1.86 ECTS] AF8 - Partial and final exams [3 hours, 100% face-to-face, 0.1 ECTS] METHODOLOGY MD1 - Class lectures by the professor with the support of computer and audiovisual media, in which the main concepts are developed and the bibliography is provided to complement the students' learning. MD2 - Critical reading of texts recommended by the professor of the subject: press articles, reports, manuals and/or academic articles, either for later discussion in class, or to expand and consolidate the knowledge of the subject. MD3 - Resolution of practical cases, problems, etc. .... posed by the teacher individually or in groups. MD5 - Preparation of papers and reports individually or in groups.
Assessment System
  • % end-of-term-examination 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100
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.