Learning techniques in Planning

author: Rao Kambhampati, Arizona State University
published: Feb. 25, 2007,   recorded: February 2006,   views: 8913

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.

 Watch videos:   (click on thumbnail to launch)

Watch Part 1
Part 1 56:01
Watch Part 2
Part 2 47:16
Watch Part 3
Part 3 56:07
Watch Part 4
Part 4 19:50


In this lecture, I aim to provide an overview of the learning techniques that have found use in automated planning. Unlike most the clustering and classification tasks that have dominated the recent machine learning literature, learning in planning requires handling relational and first order representations, and foregrounds the need for knowledge-intensive learning techniques. I will start with a brief review of the planning models, and discuss the opportunities for learning in planning. I will then provide a survey of the explanation-based, case-based and inductive learning techniques that have been successfully used to tackle them.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: