Course Description

Prerequisites. Students taking this course should have knowledge of object-oriented analysis, modeling and design, relational databases, and basic notions of logic and discrete mathematics, as acquired in previous courses. Knowledge and understanding of basic artificial intelligence techniques and concepts are also required.

Content: The course provides an introduction to the theory and algorithms underlying automated planning, covering classical, numeric, diverse, temporal, and probabilistic planning, as well as emerging GenAI approaches to planning. 

Automated planning is a branch of AI concerned with generating strategies or sequences of actions that transform a given initial state into a desired goal state. A key feature of automated planning is its reliance on an explicit model of the problem domain, which represents the relevant states, available actions, and their effects. Central to automated planning is reasoning about actions, which involves determining the executability of action sequences and projecting their effects on future states. These reasoning capabilities enable intelligent agents to identify and execute courses of action that lead from their current state to one that satisfies their goals. 

Objectives. The students will learn the theoretical and algorithmic foundations of automated planning and their practical implementation. They will understand the fundamental concepts underlying modern planning algorithms, and they will be equipped to conduct projects in this area.

Intended learning outcomes. After the course, the student will be able to:

• Evaluate and apply a variety of planning techniques for real-world domains.

• Explain the practical advantages and disadvantages of different levels of expressivity in planning models.

• Model classical and non-convential planning problems in commonly used domain definition languages.

• Solve planning problems with automatic solvers.

Main Topics. 

• Introduction to the Classical Planning model

• Languages for Classical Planning

• Search Algorithms: Blind and Heuristic

• Domain-Independent Heuristics and Relaxations

• Classical Planning: Complexity and Extensions

• Non-classical planning variants: Numeric, Temporal, Diverse, Probabilistic Planning

• Generative AI approaches to planning 

• Getting to Know and Use a Planner 

Teaching material. 

[1] Course slides, notes, and additional material available on this site. 

[2] Artificial Intelligence: A Modern Approach, Global Edition, 4th Edition by Stuart Russell, Peter Norvig, Pearson, 2020

[3] A Concise Introduction to Models and Methods for Automated Planning, by Hector Geffner and Blai Bonet, Springer, 2013

[4] Automated Planning: Theory and Practice, by Malik Ghallab, Dana Nau, Paolo Traverso, Elsevier, 2001

[5] An Introduction to the Planning Domain Definition Language, by Patrik Haslum, Nir Lipovetzky, Daniele Magazzeni, Christian Muise, Springer, 2019