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- 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

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 6.4 Timeline-based planning

Learning activities: AF1: Lectures AF2: Lab exercises AF5: Tutorship AF6: Work in groups AF7: Individual and autonomous work AF8: Final and partial exams Teaching Methodologies: MD1: Classroom Lectures (in a synchronous online teaching mode) using computer and audiovisual aids to develop the main concepts of the subject, with bibliographic references provided to supplement student learning. MD2: Critical Reading of texts recommended by the course professor. MD3: Case Study and Problem Solving of practical cases, problems, etc., posed by the professor, either individually or in groups. MD4: Exposition and discussion in class, moderated by the professor, on topics related to the course content as well as practical cases. MD5: Preparation of projects and reports, either individually or in groups.