TDDD48 Automated Planning

Teaching Machines to Think

Welcome to the course website!

Automated planning is a central topic in AI that deals with intelligent sequential decision making. It is the task of automatically deciding which sequence of actions needs to be applied to reach a given set of goals. Planning technology is currently used with great success in applications ranging from production lines and elevators to unmanned aerial vehicles (UAVs) and space applications such as the Hubble Space Telescope and the Mars rovers. The aim of this course is to provide a comprehensive view of state-of-the-art planning techniques, as well as hands-on experience in constructing and modeling planning domains to solve specific planning problems.

Sessions that have a date in front of them have updated slides already.

Lectures

# Date Chapter Title
1 2026-04-17 A1 Organizational Matters (not part of exam)
A2 What is Planning? (not part of exam)
2 2026-04-22 A3 Getting to Know a Planner (not part of exam)
B1 Transition Systems and Propositional Logic
B2 Introduction to Planning Tasks
3 2026-04-24 B3 Formal Definition of Planning
C1 Overview of Classical Planning Algorithms
C2 Progression and Regression Search
4 2026-04-29 D1 Delete Relaxation: Relaxed Planning Tasks
D2 Delete Relaxation: Finding Relaxed Plans
D3 Delete Relaxation: Relaxed Task Graphs
D4 Delete Relaxation: hmax and hadd
D5 Delete Relaxation: Analysis of hmax and hadd (skipped in 2026, not part of exam)
D6 Delete Relaxation: hFF and Comparison of Heuristics
5 2026-05-06 E1 Planning Tasks in Finite-Domain Representation
E3 Abstractions: Introduction
E4 Abstractions: Formal Definition and Heuristics
E5 Abstractions: Orthogonality and Additivity (skipped in 2026, not part of exam)
E6 Pattern Databases
6 2026-05-08 E7 Merge-and-Shrink: Factored Transition Systems
E8 Merge-and-Shrink: Algorithm
E9 Merge-and-Shrink: Strategies and Label Reduction
7 2026-05-13 F1 Constraints: Introduction
F2 Landmarks: RTG Landmarks
F3 Landmarks: Minimum Hitting Set Heuristic
F4 Landmarks: Cut Landmarks & LM-Cut Heuristic
8 2026-05-20 F5 Linear & Integer Programming
F6 Cost Partitioning
F7 Optimal and General Cost Partitioning
9 2026-05-22 F8 Post-hoc Optimization
F9 Network Flow Heuristics
F10 Operator Counting
F11 Potential Heuristics
Z1 Planning the Future (not part of exam)
10 2026-05-27 Z2 Discussing the solution of the demo exam

Labs

In 2026, there are five labs. Labs with links are finalized for the 2026 iteration.

Due Date Material
2026-04-27 8:00 am Lab 1, Vagrantfile
2026-05-04 8:00 am Lab 2
2026-05-11 8:00 am Lab 3
2026-05-18 8:00 am Lab 4
2026-05-25 8:00 am Lab 5
skipped in 2026 Lab 6

Exam

You may prepare and use one sheet of A4 paper with notes, filled manually or by printing, and using one or both sides. Other aids such as lecture slides, books, or calculators are not allowed. All electronic devices must be turned off during the exam.

Here is a demo exam, whose solution we will discuss shortly before the exam.

Example planning task

Visualization of an example planning task for a household robot. The robot nees to transport packages between locations in different rooms.

Opportunities in the Machine Reasoning Lab

MSc and PhD projects: https://mrlab.ai/positions/