Coursera

Course participants are encouraged to use the Discussion Forums and the Course Wiki to seek advice and provide helpful comments or pointers to useful resources to other course members. Please do not give away solutions to quizzes or assessments here, or elsewhere.

The Course Twitter Tag is #aiplan. The course team Twitter username is @aiplanner.

The course social platform, including an experimental virtual world meeting space, is described more fully on the Wiki Social page.

Data about the course, participation maps and other statistics are gathered on the Course Data Page.

Week-by-Week and Timing

A course study week runs from a Monday 00:00 UTC to the following Sunday 23:59 UTC. Video lectures and related materials will be made available each week. To assist participants to flexibly schedule their studies, we will publish the materials for each week on the preceding Friday 09:00 UTC. Quizzes and exams are due on Sundays 23:59 UTC, with a 24 hours "hard deadline" submission flexibility to allow for time zone variances. Please ensure you make your submissions by the published due dates to avoid losing your marks.**

Week 1: Introduction and Planning in Context

Week 2: State-Space Search: Heuristic Search and STRIPS

Week 3: Plan-Space Search and HTN Planning

— Break —

Week 4: Graphplan and Advanced Heuristics

Week 5: Plan Execution and Applications

The anticipated workload is 5 to 6 hours a week for the "Foundation Level" core material in the course. However students following the "Awareness Level" may complete the course in significantly less time. Those wishing to pursue the "Performance Level" and carry out the programming or creative assignments can expect to spend significantly longer working on this material.

** If you are on UTC+1 and the time in your zone shows as 12:59 AM, do not get confused, remember that is just after midnight, and not just after midday!

Recommended Background

The course can be studied at different levels, but you will need a basic understanding of logic and mathematical reasoning. Optional programming assignments require programming skills

The course can be augmented using readings from two text books, but these are not required for the course:

"Automated Planning: Theory and Practice" by Malik Ghallab, Dana Nau and Paulo Traverso (Elsevier, ISBN 1-55860-856-7) 2004.
"Artificial Intelligence: A Modern Approach" by Stuart Russell and Peter Norvig (Prentice Hall, ISBN 0-13-604259-7) 2010. (Note that Chapter 11: Planning is available online)

A number of other Course Readings are suggested as the course progresses, and links to freely acessible on-line PDF copies are provided where possible.

Levels of Engagement

You can engage with the course at a number of levels, depending on your interests and level of access to suitable computer systems.

Participation Level	Study Time	Requirement for Completion
Awareness Level	2-3 hours per week	Engage with nominated videos marked with ♦ (details described on the "Assessments and Exams" page), watch the weekly feature videos, and complete a final awareness level exam on their contents.
Foundation Level	5-6 hours per week	Engage with the core course material and receive an acceptable score with a combination of the Awareness Level and Foundation Level Final Exams.
Performance Level	8 hours per week or more	Receive an acceptable score on the total of your Awareness Level and Foundation Level Final Exams on the course, and the marks you gain on the Performance Level assignments, which will require writing code, running an AI planner or producing an appropriate digital artifact.
Continuation Level	Up to you	We will encourage previous students to join in future sessions to maintain contact with the community, continue their studies and view new material or features.

Assessments and Completion

Our main aim in the quizzes, homework and exams is to check and measure your level of engagement with the course materials and suggested readings or AI planner use. We do want you to understand the content and to make the material useful to you.

Mid course quizzes (checks) in week 3 will give feedback on your progress, but will not be taken into account for the final course completion marks or assessment. But take care with the final exams in week 5, and they must be submitted by the due date. A one day allowance beyond the indicated due date is given in all cases for assessments to allow for time zone variances.

For the Awareness Level there is a final exam in week 5. Feedback is given and you may repeat the final awareness exam a number of times if necessary.

The Foundation Level of the course represents the core of the taught material, and is assessed through a final exam in week 5.

For the Performance Level there will be a number of programming assignments and an additional creative challenge. These will be given during weeks two to four of the course. You will be required to write code, run an AI planner, or produce an appropriate digital artifact for this optional level of the course. No specific programming language is required for the course. You may use any programming language that you feel is appropriate for the programming assignments.

Statement of Accomplishment and Course Badges

Students who successfully complete the class at any level of engagement will be offered a Statement of Accomplishment signed by the instructors, and an appropriate Course Badge .

Levels, Grades and Scores

The week 5 Awareness Level exam provides up to 40 points. 35 points or more are required for an Awareness level pass.

The week 5 Foundation Level exam provides up to 30 points. Your Awareness Level score contributes towards the Foundation Level score. 60 points or more in total are required for a Foundation Level pass.

Your Awareness Level and Foundation Level scores contribute up to 70 marks towards the Performance Level score. The week 2 and week 4 Programming Assignments and the week 2 Creative Challenge Performance Level assignments provide up to 10 points each. 75 points or more required for a Performance Level pass.

Week 3 quizzes (checks), homework and all in-line video quizzes have no effect on your final score and are there to assist you with monitoring your progress and understanding.

Course Team

	Dr. Gerhard Wickler: Obtained his Ph.D. in 1999 at Edinburgh in the area of AI Planning. He went on to hold research positions in Italy, Belgium, and Germany, working in several areas of AI. Since 2004 he has been senior researcher at the Artificial Intelligence Application Institute (AIAI) within the School of Informatics at the University of Edinburgh, where he teaches the AI Planning course. Dr. Wickler regularly publishes in AI-related conferences and journals, reporting on his research in AI Planning and Intelligent Agents applied to emergency response. He is an active reviewer for a number of conferences and journals. He has been a member of the programme committees for various workshops and conferences, including the Intelligent Systems track at ISCRAM. He is currently lead scientist on an EPSRC and industry funded Autonomous and Intelligent Systems project using AI plan modelling in dynamic environments. In May 2010, he was elected onto the board of directors of the ISCRAM Association and has received the ISCRAM Distinguished Service Award.
	Prof. Austin Tate: Director of the Artificial Intelligence Applications Institute (AIAI) and holds the Personal Chair of Knowledge-Based Systems at the University of Edinburgh. He is a Fellow of the Royal Academy of Engineering, Fellow of the Royal Society of Edinburgh, Fellow of the Association for the Advancement of AI, Fellow of the British Computer Society, Senior Visiting Research Scientist at the Institute of Human & Machine Cognition (IHMC) in Florida, and on the advisory board for IEEE Intelligent Systems. His research involves advanced knowledge systems and planning technologies, with a focus on their use in emergency response and collaboration especially using virtual worlds. He is the Coordinator for the Virtual University of Edinburgh (Vue) and Coordinator for Distance Education in the School of Informatics at the University of Edinburgh.
Course Staff: Herry, Miles Gould and Punyanuch Borwarnginn ("Pun"). Thanks also to those members of the class and AI planning community who are supporting fellow students by acting as Community TAs.