CS6260: Automated Planning and Learning

Jan - May 2026

Course Information

Offered for: BTech, MTech, MS, PhD
Instructor: Pulkit Verma
Teaching Assistant: Sayak Sen, Swapnil S. Sonawane, and Tushar
Room: CS 15, CSB (Ground floor)
Class Times:
Tuesday: 11:00 - 11:50 AM
Wednesday: 10:00 - 10:50 AM
Thursday: 8:00 - 8:50 AM
Friday: 5:00 - 5:50 PM
Pre-requisite Skills: Familiarity with Logic, Programming, Analysis of Algorithms, and Probability
Grading Policy:
Quiz 1: 20%
Quiz 2: 20%
End-semester: 30%
In class tasks: 5%
Assignments: 25%
Course Email: cs6260.jm26@gmail.com
Course Handout (PDF)

Objectives

This course aims to introduce students to the theoretical foundations and practical techniques of automated planning. It covers a range of planning paradigms, from classical deterministic planning to advanced forms, including:

  • Temporal and durative actions
  • Planning under uncertainty
  • Hierarchical planning
  • Autonomously learning the underlying models for various planning paradigms

The overall goal is to enable AI systems to automatically generate sequences of high-level actions to achieve desired goals.

Course Contents

Module 1: Deterministic Planning [3 Weeks]

  • Introduction, Complexity, PDDL (Planning Domain Definition Language)
  • Planning with Deterministic Models
  • Heuristics, Planners, Planning Graphs
  • PDDL 3.0: Features and Planners
  • PDDL 3.1: Object Fluents and Practical Limitations to Adoption

Module 2: Hierarchical and Multi-Agent Planning [2 Weeks]

  • Hierarchical Task Networks (HTNs)
  • HDDL (Hierarchical Domain Definition Language) and HTN Planning
  • MA-PDDL (Multi-Agent PDDL) and Cooperative Multi-Agent Planning

Module 3: Planning under Uncertainty [3 Weeks]

  • Markov Decision Processes (MDPs) and RL Basics
  • FOND (Fully-Observable Non-Deterministic) Planning
  • Probabilistic Planning and PPDDL (Probabilistic PDDL)

Module 4: Learning for Planning [4 Weeks]

  • Learning Heuristics
  • Learning Deterministic Action Models
  • Learning HTN Methods
  • Learning PPDDL and NDRs (Noisy Deictic Rules)
  • Learning Predicates/Symbols for Planning

Module 5: Practical Applications (Some of the following topics depending on time) [2 Weeks]

  • PDDL for RL
  • PDDL for Task and Motion Planning (TAMP)
  • Large Language Models (LLMs) for PDDL Planning
  • Advanced applications of PDDL

Text Books

No single book covers all modules, so chapters from a couple of books will be referred to.

[APL] Acting, Planning, and Learning. Malik Ghallab, Dana Nau, and Paolo Traverso. Cambridge University Press, 2025. Freely available here.

[PDDL] An Introduction to the Planning Domain Definition Language. Patrik Haslum, Nir Lipovetzky, Daniele Magazzeni, and Christian Muise. Morgan & Claypool Publishers, 2019. Freely available through IITM here.

Other Reference Books

[MMAP] A Concise Introduction to Models and Methods for Automated Planning. Hector Geffner and Blai Bonet. Springer Nature, 2022. Freely available through IITM here.

[APA] Automated Planning and Acting. Malik Ghallab, Dana Nau, and Paolo Traverso. Cambridge University Press, 2016. Freely available here.

[APTP] Automated Planning: Theory and Practice. Malik Ghallab, Dana Nau, and Paolo Traverso. Morgan Kaufmann Publishers, 2004.

[AIMA] Artificial Intelligence: A Modern Approach, 3rd Edition. Stuart Russell and Peter Norvig. Prentice Hall, 2009. (Better for automated planning than the 4th edition)

Course Calendar

# Date Lecture Material Readings HW
1. Tue, Jan 20 Introduction Slides APL Chapter 1 -
2. Fri, Jan 23 Deterministic Search Slides AIMA Chapter 2 and Sections 3.1-3.3 -
3. Tue, Feb 03 Informed Search Slides AIMA Sections 3.4-3.5 -
4. Web, Feb 04 Informed Search 2: A* Search Slides AIMA Sections 3.5-3.6 -
5. Thu, Feb 05 Classical Planning Slides APL Sections 2.1-2.4 -
6. Tue, Feb 10 Planning Domain Definition Language (PDDL) Slides PDDL Section 2.1 -
7. Web, Feb 11 Heuristics for Planning Slides AIMA Section 10.3 -
8. Thu, Feb 12 Heuristics for Planning 2 Slides AIMA Section 10.2 - 10.3 -
9. Fri, Feb 13 PDDL 1.2 Features Slides PDDL Chapter 3 -
10. Tue, Feb 17 Temporal Planning: Durative Actions 1 Slides PDDL Chapter 4 and Section 5.1 -
11. Fri, Feb 20 Temporal Planning: Durative Actions 2 Slides PDDL Chapter 4 and Section 5.1 -
12x. Feb 24 - Mar 06 PDDL Domain Model Learning Video APL Chapter 4 -
12. Wed, Mar 11 HTN Planning Slides APL Chapter 5 -
13. Fri, Mar 13 HTN Planning 2 Slides APL Chapter 5 -
14. Tue, Mar 17 HTN Planning 3 Slides APL Chapter 5 -
15. Wed, Mar 18 Planners for TOHTN Planning Slides -
16. Thu, Mar 19 Partial Order HTN Planning Slides APL Chapter 5 -
17. Fri, Mar 20 Acting with HTNs Slides APL Chapter 6 -
18. Fri, Mar 20 Hierarchical Domain Definition Language (HDDL) Slides (Refer to last slide) -
19. Tue, Mar 24 Learning PDDL Models Slides -
20. Wed, Mar 25 Learning Object-Centred Models (LOCM) Slides Paper -
21. Thu, Mar 26 Learning Object-Centred Models II Slides Paper -
22. Fri, Mar 27 Learning Object-Centred Models III Slides Paper -
23. Web, Apr 01 LOCM2 Slides Paper -
24. Tue, Apr 07 FAMA: Model Learning as Planning Slides Paper -
25. Wed, Apr 08 Active Model Learning Slides Paper -
26. Thu, Apr 09 Agent Interrogation Algorithm Blackboard Image Paper -
27. Wed, Apr 15 Agent Interrogation Algorithm 2 Slides Paper -
28. Thu, Apr 16 Markov Decision Processes Slides AIMA Section 17.1 -
29. Fri, Apr 17 Solving MDPs: Value Iteration Slides AIMA Section 17.2.1 -
30. Fri, Apr 17 Solving MDPs: Policy Iteration and Evaluation Slides AIMA Section 17.2.2-17.2.4 -
31. Tue, Apr 21 FOND Planning Slides Paper -
32. Wed, Apr 22 PPDDL and Learning PPDDL Slides Paper 1, Paper 2 -
33. Tue, Apr 28 LLMs for Planning Slides Paper 1, Paper 2, Paper 3 -
34. Wed, Apr 29 Motion Planning Slides -
35. Thu, Apr 30 Integrated Task and Motion Planning Slides -
36. Tue, May 05 Course Summary and Review Slides -