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Artificial Intelligence Planning

The course aims to provide a foundation in artificial intelligence techniques for planning, with an overview of the wide spectrum of different problems and approaches, including their underlying theory and their applications. The January 2015 session was the final version of the course. It will remain open so that those interested can register and access all the materials.

Preview Lectures

Sessions

Eligible for

Statement of Accomplishment

Course at a Glance

About the Course

The course aims to provide a foundation in artificial intelligence techniques for planning, with an overview of the wide spectrum of different problems and approaches, including their underlying theory and their applications. It will allow you to:

  • Understand different planning problems
  • Have the basic know how to design and implement AI planning systems
  • Know how to use AI planning technology for projects in different application domains
  • Have the ability to make use of AI planning literature

Planning is a fundamental part of intelligent systems. In this course, for example, you will learn the basic algorithms that are used in robots to deliberate over a course of actions to take. Simpler, reactive robots don't need this, but if a robot is to act intelligently, this type of reasoning about actions is vital.

Course Syllabus

Week 1: Introduction and Planning in Context
Week 2: State-Space Search: Heuristic Search and STRIPS
Week 3: Plan-Space Search and HTN Planning
One week catch up break
Week 4: Graphplan and Advanced Heuristics
Week 5: Plan Execution and Applications
Exam week

The January 2015 session was the final version of the course. It will remain open so that those interested can register and access all the materials.

Recommended Background

The MOOC is based on a Masters level course at the University of Edinburgh but is designed to be accessible at several levels of engagement from an "Awareness Level", through the core "Foundation Level" requiring a basic knowledge of logic and mathematical reasoning, to a more involved "Performance Level" requiring programming and other assignments.

Suggested Readings

The course follows a text book, but this is not required for the course: 
Automated Planning: Theory & Practice (The Morgan Kaufmann Series in Artificial Intelligence) by M. Ghallab, D. Nau, and P. Traverso (Elsevier, ISBN 1-55860-856-7) 2004.

Course Format

Five weeks of study comprising 10 hours of video lecture material and special features videos. Quizzes and assessments throughout the course will assist in learning. Some weeks will involve recommended readings. Discussion on the course forum and via other social media will be encouraged. A mid-course catch up break week and a final week for exams and completion of assignments allows for flexibility in study.

You can engage with the course at a number of levels to suit your interests and the time you have available:

  • Awareness Level - gives an overview of the topic, along with introductory videos and application related features. This level is likely to require 2-3 hours of study per week.
  • Foundation Level - is the core taught material on the course and gives a grounding in AI planning technology and algorithms. This level is likely to require 5-6 hours of study per week of study.
  • Performance Level - is for those interested in carrying out additional programming assignments and engaging in creative challenges to understand the subject more deeply. This level is likely to require 8 hours or more of study per week.

FAQ

  • When will the course run again?

    The January 2015 session was the final version of the course. It will remain open so that those interested can register and access all the materials. All assignments are available to try but will not score or be eligible for a statement of accomplishment

  • Will I get a certificate after completing this class?

    Students who complete the class during the originally scheduled session dates (that is, by 1st March 2015) will be offered a Statement of Accomplishment signed by the instructors.

  • Do I earn University of Edinburgh credits upon completion of this class?

    The Statement of Accomplishment is not part of a formal qualification from the University. However, it may be useful to demonstrate prior learning and interest in your subject to a higher education institution or potential employer.

  • What resources will I need for this class?

    Nothing is required, but if you want to try out implementing some of the algorithms described in the lectures you'll need access to a programming environment. No specific programming language is required. Also, you may want to download existing planners and try those out. This may require you to compile them first.

  • Can I contact the course lecturers directly?

    You will appreciate that such direct contact would be difficult to manage. You are encouraged to use the course social network and discussion forum to raise questions and seek inputs. The tutors will participate in the forums, and will seek to answer frequently asked questions, in some cases by adding to the course FAQ area.

  • What Twitter hash tag should I use?

    Use the hash tag #aiplan for tweets about the course.

  • How come this is free?

    We are passionate about open on-line collaboration and education. Our taught AI planning course at Edinburgh has always published its course materials, readings and resources on-line for anyone to view. Our own on-campus students can access these materials at times when the course is not available if it is relevant to their interests and projects. We want to make the materials available in a more accessible form that can reach a broader audience who might be interested in AI planning technology. This achieves our primary objective of getting such technology into productive use. Another benefit for us is that more people get to know about courses in AI in the School of Informatics at the University of Edinburgh, or get interested in studying or collaborating with us.