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TDDD48-2021 Reading Instructions

The main literature for this course is the book Automated Planning: Theory and Practice by Malik Ghallab, Dana Nau and Paolo Traverso. The current printing of the book contains a number of errors – please check the errata list! We may also provide additional references for specific topics.

Some of the material discussed in the book will also be covered during the lectures. However, some of the essential issues discussed in the book are more suitable for reading at your own pace. Lectures are a complement to the book, not a replacement!

At the same time, you should be aware that the lectures cover some topics that are not discussed in the book.

Focus on understanding

Understanding terminology and basic notation is essential. Remembering the more esoteric functions defined in the book is less essential. Here are a few examples illustrating this focus.

  • Notation: You must be able to read and understand basic notation such as γ(s,a) for the state transition function and S for the set of states in a state transition system. These symbols are used in many places throughout the book and will also been used during the lectures. You must also understand the concept of reachable states, but you do not necessarily have to remember the notation Γ^(s), since this is less commonly used and not as central to your understanding.

  • You must understand and be able to explain the general structure of a state transition system and its components (states, actions, transition function). However, you don't necessarily have to remember if the transition system is defined as Σ=(S,A,γ) or Σ=(A,S,γ).

  • You should understand the various restrictions we place on the state transition system for classical planning (A0-A7), and if asked whether classical planning allows (for example) non-deterministic actions, you must know the answer. However, you don't necessarily have to be able to enumerate all restrictions by heart.

  • You should also understand and be able to explain how algorithms such as TFD and PFD work, and why choosing a single method to expand in PFD can lead to many successor networks (second line from the end in Figure 11.9, page 243). But remembering the order of the arguments to δ(w,u,m,σ) in PFD is irrelevant.

Though we focus less on formal notation, you still need to be able to clearly demonstrate what you have understood. For example, if you are asked to explain a particular method for planning, such as PFD, your explanation should be sufficiently strict, detailed, coherent and comprehensible that (a) someone who is not familiar with the area easily understands the main issues involved, and (b) someone familiar with the area sees that you have understood the critical characteristics of the method. It should be possible to apply the explanation to a simple example and see how it works. As long as you can do this, being able to write down the exact definition of an algorithm in the book from memory in pseudo-code is less important – but using pseudo-code is of course one possible way of being strict and detailed in a description!

Reading Instructions

What are you expected to learn? As in any course, it is naturally impossible to give you an exact definition of the relative importance of every issue covered in the book. However, we can still give you some general guidelines through the reading instructions below.

In addition to the chapter and section numbes, the reading instructions include some examples of what we focus on in particular. These are only examples, not an exhaustive list of everything you need to know in each chapter.

  • Chapter 1: Introduction and Overview.
    Appendix A: Search Procedures and Computational Complexity.
    Appendix B: First-Order Logic.
    The first chapter provides a useful introduction to various forms of planning. Reading it will help you understand the remainder of the book.

    Also, please take a look at Appendixes A and B to ensure that you have the basic prerequisites for taking this course. In particular, sections A.1, A.2 and B.[1-3] are essential.

  • Chapter 2: Representations for Classical Planning.
    We will mostly be using the classical and state-variable representations. However, a basic understanding of the set-theoretic representation (2.2) will also be useful in order to fully understand the properties of the other representations.

    You must understand these representations to the extent that you can use them to model various planning problems.

    Again, notatation is less important than understanding concepts. You do have to understand common notation that has been used in the lectures, such as γ(s,a), π, S=(Σ,s0,g), and so on. If we use less common notation, such as Γ(s) [page 22], it will be briefly explained: The exam will remind you that "Γ(s) is the set of all successors of s", or "Γ-1(g) is defined as { γ-1(g,a) | a is relevant for g }". You will still have to understand the concepts "successor", "relevant action for a goal", and so on.

    Sections 2.4.5-2.4.8 are not included.

  • Chapter 3: Complexity of Classical Planning.
    Formal complexity analysis is no longer part of the course.

  • Chapter 4: State-Space Planning.
    Chapter 5: Plan-Space Planning.
    These chapters provide a number of fundamental concepts used in a very large number of planners, and are consequently very important for your understanding of the material in the remainder of the course.

    Lifted versions of planning are also included in the course, to the extent that they are included in the lectures.

    Section 4.4, on STRIPS, is not included. Sections 5.4.2, on the PoP procedure, and 5.5, on extensions to partial-order planning, are not included.

  • Chapter 6: Planning-Graph Techniques.
    You do not need to know general planning graph techniques. However, you do need to understand the application of planning graphs in Relaxed Planning Graph heuristics.

  • Chapter 7: Propositional Satisfiability Techniques.
    You need to understand the basic concepts and techniques involved in converting classical planning problems into satisfiability problems (7.2), including but not limited to action encodings, bounded planning problems, and frame axioms.

    The specific details of the Davis-Putnam algorithm or other satisfiability algorithms are not part of the course (7.3). Section 7.4 is not included.

  • Chapter 8: Constraint Satisfaction Techniques.
    Not included in the course.

  • Chapter 9: Heuristics in Planning.
    Heuristics are a very important concept that should be understood in detail, especially for state-space planning.

    For example, you need to understand the concept of relaxation and to distinguish between different types of relaxation. You need to understand how forward and backward search can benefit from the use of heuristics.

    Section 9.4 is not included. You no longer need to understand how the hm heuristics work. These have been replaced with newer heuristics in the lectures.

  • Chapter 10: Control Rules in Planning.
    Chapter 11: Hierarchical Task Network Planning.
    These chapters show how planning can be guided using two separate forms of domain knowledge.

    Apart from understanding the use of control rules or HTN methods in isolation, you should also understand key issues such as how formulas can be progressed through states, how pruning differs from using heuristics and how HTNs and control rules lead to different domain models due to HTNs being based on "recipes" specifying good actions and control rules being based on formulas forbidding bad actions.

    Sections 10.5, 10.6, 11.5, 11.6.1, 11.7, and 11.8 are not included. Note that Section 11.6.2 is included.

  • Chapter 12: Control Strategies in Deductive Planning.
    Not included in the course.

  • Chapter 13: Time for Planning.
    Chapter 14: Temporal Planning.
    Chapter 15: Planning and Resource Scheduling.
    Not included in the course.

  • Chapter 16: Planning Based on Markov Decision Processes.
    Chapter 17: Planning Based on Model Checking.
    Appendix C: Model Checking.
    Chapter 18: Uncertainty with Neoclassical Techniques.

    Sections 16.1 and 16.2 are included in the course. Sections 16.3 and 16.4 are not included.

    You should be able to apply policy iteration and value iteration to a simple problem instance.

    Chapter 17 and the associated Appendix C are not included.

    Chapter 18 is not included.

  • Chapter 19: Space Applications.
    Chapter 21: Planning for Manufacturability Analysis.
    Chapter 22: Emergency Evacuation Planning.
    Chapter 23: Planning in the Game of Bridge.
    These chapters are not included in the course, but provide an interesting view of different ways in which automated planning can be applied to solve real-world problems.

  • Chapter 20: Planning in Robotics.
    Section 20.2, covering path and motion planning, is included in the course. The course slides on path planning are also included.

  • Chapter 24: Other Approaches to Planning.
    This chapter provides a high-level overview of various important aspects of planning and is part of the course to the extent that you should be aware of the issues discussed in the chapter. There will be no exam questions specifically on this material.

Page responsible: Jonas Kvarnstr�m
Last updated: 2021-04-07