An Introduction to the Planning Domain Definition Language / by Patrik Haslum, Nir Lipovetzky, Daniele Magazzeni, Christian Muise.

Format:

Book

Author/Creator:

Haslum, Patrik., Author.

Lipovetzky, Nir., Author.

Magazzeni, Daniele., Author.

Muise, Christian., Author.

Series:

Synthesis Lectures on Artificial Intelligence and Machine Learning, 1939-4616

Language:

English

Subjects (All):

Artificial intelligence.

Machine learning.

Neural networks (Computer science).

Artificial Intelligence.

Machine Learning.

Mathematical Models of Cognitive Processes and Neural Networks.

Local Subjects:

Artificial Intelligence.

Machine Learning.

Mathematical Models of Cognitive Processes and Neural Networks.

Physical Description:

1 online resource (XVII, 169 p.)

Edition:

1st ed. 2019.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2019.

Summary:

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.

Contents:

Praise for An Introduction to the Planning Domain Definition Language

Preface

Introduction

Discrete and Deterministic Planning

More Expressive Classical Planning

Numeric Planning

Temporal Planning

Planning with Hybrid Systems

Conclusion

Bibliography

Authors' Biographies

Index .

ISBN:

9783031015847

3031015843