Planning is the branch of Artificial Intelligence (AI) that seeks to automate reasoning about plans, most importantly the reasoning that goes into formulating a plan to achieve a given goal in a given situation. AI planning is model-based: a planning system takes as input a description (or model) of the initial situation, the actions available to change it, and the goal condition to output a plan composed of those actions that will accomplish the goal when executed from the initial situation.
The Planning Domain Definition Language (PDDL) is a formal knowledge representation language designed to express planning models. Developed by the planning research community as a means of facilitating systems comparison, it has become a de-facto standard input language of many planning systems, although it is not the only modelling language for planning. Several variants of PDDL have emerged that capture planning problems of different natures and complexities, with a focus on deterministic problems.
The purpose of this book is two-fold. First, we present a unified and current account of PDDL, covering the subsets of PDDL that express discrete, numeric, temporal, and hybrid planning. Second, we want to introduce readers to the art of modelling planning problems in this language, through educational examples that demonstrate how PDDL is used to model realistic planning problems. The book is intended for advanced students and researchers in AI who want to dive into the mechanics of AI planning, as well as those who want to be able to use AI planning systems without an in-depth explanation of the algorithms and implementation techniques they use.
Author(s): Patrik Haslum, Nir Lipovetzky, Daniele Magazzeni
Series: Synthesis Lectures on Artificial Intelligence and Machine Learning
Edition: 1
Publisher: Morgan & Claypool Publishers
Year: 2019
Language: English
Commentary: Vector PDF
Pages: 188
City: San Rafael, CA
Tags: Artificial Intelligence; Planning; Domain-Specific Languages
Praise for An Introduction to the Planning Domain Definition Language
Preface
Introduction
What is AI Planning?
Planning Models
Examples
The Knight's Tour
Logistics
Plans as Explanations
Plan-Based Control
Planning in Robotics
Narrative Planning
The Origins of PDDL and the Scope of This Book
Other Planning Languages
Relation to Non-Planning Formalisms and Other Fields
Planning Systems and Modelling Tools
Discrete and Deterministic Planning
Domain and Problem Definition
PDDL: First Example
Example: Modelling the Knight's Tour
Example: Logistics
Plans and Plan Validity
Sequential Plans
Non-Sequential Plans
Notes on PDDL's Syntax: The STRIPS Fragment
Advanced Modelling Examples
Sorting by Reversals
Deadlock Detection
Expressiveness and Complexity
Plan Validation
Bounds on Plan Length: Ground STRIPS
Computational Complexity of Ground STRIPS Planning
Computational Complexity of Parameterised STRIPS Planning
Reversal and Complement
Practical Considerations
More Expressive Classical Planning
Conditional and Quantified Conditions and Effects
Basic Elevator Model
Conditional Effects
Universally Quantified Effects
Disjunctive and Existentially Quantified Preconditions
Universally Quantified Preconditions and Goals
Axioms
Preferences and Plan Quality
State Trajectory Constraints
Expressiveness and Complexity
Numeric Planning
Numeric Planning in PDDL
Numeric Plan Validity
More Modelling Examples
Complexity of Numeric Planning
Some Undecidable Cases
Some Decidable Cases
Numeric Planning and Other Extensions
Temporal Planning
Durative Actions
Planning with Predictable Events
Temporal Plan Validity
Combining Numeric and Temporal Planning
Planning with Hybrid Systems
Continuous Processes
Exogenous Events
Example: The Generator
Example: Multiple-Battery Management
Kinetic Battery Model
PDDL+ Model for the Kinetic Battery
Plan Validation in Hybrid Domains
Modelling Assumption in PDDL+
Conclusion
Other Planning PDDL-Like Languages
The Future of PDDL
Online PDDL Resources
Bibliography
Authors' Biographies
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