Undergraduate Course: Automated Planning (Level 10) (INFR10045)
|School||School of Informatics
||College||College of Science and Engineering
||Availability||Available to all students
|Credit level (Normal year taken)||SCQF Level 10 (Year 4 Undergraduate)
|Home subject area||Informatics
||Other subject area||None
||Taught in Gaelic?||No
|Course description||The 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.
Information for Visiting Students
|Displayed in Visiting Students Prospectus?||Yes
Course Delivery Information
|Not being delivered|
Summary of Intended 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.
|Written Examination 70 |
Assessed Assignments 30
Oral Presentations 0
- Practical exercise with automated planning systems
- Survey of techniques used in nominated planning systems
- Literature review of a selected area
If delivered in semester 1, this course will have an option for semester 1 only visiting undergraduate students, providing assessment prior to the end of the calendar year.
* 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
||"Automated Planning: Theory and Practice" by M. Ghallab, D. Nau, and P. Traverso (Elsevier, ISBN 1-55860-856-7) 2004.
Timetabled Laboratories 0
Non-timetabled assessed assignments 30
Private Study/Other 50
|Course organiser||Dr Amos Storkey
Tel: (0131 6)51 1208
|Course secretary||Miss Kate Weston
Tel: (0131 6)50 2701