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DRPS : Course Catalogue : School of Informatics : Informatics

Undergraduate Course: Automated Planning (Level 10) (INFR10045)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 10 (Year 4 Undergraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThe aim of this course is to provide a solid grounding in artificial intelligence techniques for planning, with a comprehensive view of the wide spectrum of different problems and approaches, including their underlying theory and their applications.
Course description Core Elements

* Introduction and overview: intuitions and motivations. Basic conceptual model for planning: state transition systems, classical assumptions. Overview of different planning problems and approaches.
* Classical planning: The classical planning problem. Situation Calculus and the Frame Problem. Classical representations and languages (e.g., STRIPS-like). Overview of State-Space Planning and Plan-Space Planning.
* Hierarchical Task Network Planning. Partial-Order Planners. Mixed-initiative Planners.
* Neoclassical Planning: Modern approaches to the classical planning problem: e.g., Planning-Graph techniques, SAT-based planning.
* Heuristics and Control Strategies: Heuristics (in state-space and plan-space planning). Hand-coded control rules and control strategies. Deductive planning and control strategies in deductive planning.
* Planning with Time and Resources: Basics of point and interval temporal algebra. Temporal constraints networks. Planning with temporal operators. Integrating planning and scheduling
* More advanced planning topics: Knowledge Engineering for Planning (including advanced representations), distributed multi-agent planning, and plan execution.
* Case Studies and Applications: A selection from robotics, manufacturing, assembly, emergency response, space exploration, games, planning for the web, etc.

Areas Covered by Self-Study and Literature Review
* Scheduling: Linear and Integer Programming. Dynamic Scheduling. Applications to real world scheduling problems. Design, development and implementation of scheduling systems.
* Planning under uncertainty: different sources of uncertainty (e.g., nondeterministic actions, partial observability). Extensions to classical approaches (e.g., plan-space, planning-graph and propositional satisfiability techniques). Planning based on Markov Decision Processes. Planning based on Model Checking.

Relevant QAA Computing Curriculum Sections: Artificial Intelligence
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Informatics 2D - Reasoning and Agents (INFR08010)
Co-requisites
Prohibited Combinations Students MUST NOT also be taking Automated Planning (Level 11) (INFR11080)
Other requirements None
Information for Visiting Students
Pre-requisitesNone
Course Delivery Information
Not being delivered
Learning Outcomes
1 - Understand and formalize different planning problems.
2 - Discuss the theoretical and practical applicability of different approaches.
3 - Have the basic know how to design and implement planning systems.
4 - Ability to review planning literature relevant to an area covered in the course.
Reading List
"Automated Planning: Theory and Practice" by M. Ghallab, D. Nau, and P. Traverso (Elsevier, ISBN 1-55860-856-7) 2004.
Additional Information
Course URL http://www.inf.ed.ac.uk/teaching/courses/plan
Graduate Attributes and Skills Not entered
KeywordsNot entered
Contacts
Course organiserDr Amos Storkey
Tel: (0131 6)51 1208
Email: A.Storkey@ed.ac.uk
Course secretaryMiss Kate Weston
Tel: (0131 6)50 2692
Email: Kate.Weston@ed.ac.uk
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