Undergraduate Course: Automated Planning (Level 10) (INFR10045)
Course Outline
| School |
School of Informatics |
College |
College of Science and Engineering |
| Course type |
Standard |
Availability |
Available to all students |
| Credit level (Normal year taken) |
SCQF Level 10 (Year 4 Undergraduate) |
Credits |
10 |
| Home subject area |
Informatics |
Other subject area |
None |
| Course website |
http://www.inf.ed.ac.uk/teaching/courses/plan
|
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
| Pre-requisites |
None |
| Displayed in Visiting Students Prospectus? |
Yes |
Course Delivery Information
|
| Delivery period: 2010/11 Semester 1, Available to all students (SV1)
|
WebCT enabled: No |
Quota: None |
| Location |
Activity |
Description |
Weeks |
Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
| Central | Lecture | | 1-11 | | | | 10:00 - 10:50 | | | Central | Lecture | | 1-11 | 10:00 - 10:50 | | | | |
| First Class |
Week 1, Monday, 10:00 - 10:50, Zone: Central. Seminar Room 6, Chrystal Macmillan Building |
| Exam Information |
| Exam Diet |
Paper Name |
Hours:Minutes |
Stationery Requirements |
Comments |
| Main Exam Diet S2 (April/May) | | 2:00 | 12 sides | c/w INFR11080. Same paper |
|
| Delivery period: 2010/11 Semester 1, Part-year visiting students only (VV1)
|
WebCT enabled: No |
Quota: None |
| Location |
Activity |
Description |
Weeks |
Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
| Central | Lecture | | 1-11 | 10:00 - 10:50 | | | | | | Central | Lecture | | 1-11 | | | | 10:00 - 10:50 | |
| First Class |
Week 1, Monday, 10:00 - 10:50, Zone: Central. Seminar Room 6, Chrystal Macmillan Building |
| Exam Information |
| Exam Diet |
Paper Name |
Hours:Minutes |
Stationery Requirements |
Comments |
| Main Exam Diet S1 (December) | | 2:00 | 12 sides | |
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.
|
Assessment Information
Written Examination 70
Assessed Assignments 30
Oral Presentations 0
Assessment
- 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. |
Special Arrangements
| None |
Additional Information
| Academic description |
Not entered |
| Syllabus |
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 |
| Transferable skills |
Not entered |
| Reading list |
"Automated Planning: Theory and Practice" by M. Ghallab, D. Nau, and P. Traverso (Elsevier, ISBN 1-55860-856-7) 2004. |
| Study Abroad |
Not entered |
| Study Pattern |
Lectures 20
Tutorials 0
Timetabled Laboratories 0
Non-timetabled assessed assignments 30
Private Study/Other 50
Total 100 |
| Keywords |
Not entered |
Contacts
| Course organiser |
Dr Amos Storkey
Tel: (0131 6)51 1208
Email: A.Storkey@ed.ac.uk |
Course secretary |
Miss Kate Weston
Tel: (0131 6)50 2701
Email: Kate.Weston@ed.ac.uk |
|
copyright 2011 The University of Edinburgh -
31 January 2011 7:52 am
|
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