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Part of the book series: Synthesis Lectures on Artificial Intelligence and Machine Learning (SLAIML)
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Table of contents (7 chapters)
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Front Matter
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Back Matter
About this book
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.
Authors and Affiliations
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Australian National University, Australia
Patrik Haslum
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University of Melbourne, Australia
Nir Lipovetzky
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King’s College, London, United Kingdom
Daniele Magazzeni
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IBM Research, USA
Christian Muise
About the authors
Daniele Magazzeni is an Associate Professor at King's College London. He received his Ph.D. in Computer Science from University of L'Aquila in 2009. His research interests are in safe, trusted, and explainable AI, with a particular focus on AI planning for robotics and autonomous systems, and human-AI teaming. He is the President-Elect of the Executive Council of the International Conference on Automated Planning and Scheduling (ICAPS).
Christian Muise is a Research Scientist at the MIT-IBM Watson AI Lab, where he researches data-driven techniques for inducing behavioural insight and leads a project devising next generation dialogue agents. Prior to this, he was a Research Fellow with the MERS group at MIT's Computer Science and Artificial Intelligence Laboratory studying decision making under uncertainty, and prior to his time at MIT, Christian was a postdoctoral fellow at the University of Melbourne's Agent lab studying techniques for multi-agent planning and human-agent collaboration. Christian completed his Ph.D. at the University of Toronto with the Knowledge Representation and Reasoning Group in the area of Automated Planning. He is the core developer and active maintainer of the Planning.Domains initiative and has a history of promoting modeling techniques for automated planning.
Bibliographic Information
Book Title: An Introduction to the Planning Domain Definition Language
Authors: Patrik Haslum, Nir Lipovetzky, Daniele Magazzeni, Christian Muise
Series Title: Synthesis Lectures on Artificial Intelligence and Machine Learning
DOI: https://doi.org/10.1007/978-3-031-01584-7
Publisher: Springer Cham
eBook Packages: Synthesis Collection of Technology (R0), eBColl Synthesis Collection 8
Copyright Information: Springer Nature Switzerland AG 2019
Softcover ISBN: 978-3-031-00456-8Published: 02 April 2019
eBook ISBN: 978-3-031-01584-7Published: 31 May 2022
Series ISSN: 1939-4608
Series E-ISSN: 1939-4616
Edition Number: 1
Number of Pages: XVII, 169
Topics: Artificial Intelligence, Machine Learning, Mathematical Models of Cognitive Processes and Neural Networks