Knowledge Engineering Tools and Techniques for AI Planning

  • Mauro Vallati
  • Diane Kitchin

Table of contents

  1. Front Matter
    Pages i-viii
  2. Knowledge Capture and Encoding

    1. Front Matter
      Pages 1-1
    2. Diego Aineto, Sergio Jiménez, Eva Onaindia
      Pages 3-20
    3. Jose Reinaldo Silva, Javier Martinez Silva, Tiago Stegun Vaquero
      Pages 47-65
    4. Volker Strobel, Alexandra Kirsch
      Pages 67-90
    5. Christian Muise, Nir Lipovetzky
      Pages 91-105
  3. Interaction, Visualisation, and Explanation

    1. Front Matter
      Pages 125-125
    2. Alfonso E. Gerevini, Alessandro Saetti
      Pages 127-155
    3. Nir Oren, Kees van Deemter, Wamberto W. Vasconcelos
      Pages 173-188
    4. Shirin Sohrabi, Octavian Udrea, Anton Riabov, Oktie Hassanzadeh
      Pages 189-207
    5. Maurício C. Magnaguagno, Ramon Fraga Pereira, Martin D. Móre, Felipe Meneguzzi
      Pages 209-227
  4. Case Studies and Applications

    1. Front Matter
      Pages 229-229
    2. Andrea Orlandini, Marta Cialdea Mayer, Alessandro Umbrico, Amedeo Cesta
      Pages 231-248

About this book


This book presents a comprehensive review for Knowledge Engineering tools and techniques that can be used in Artificial Intelligence Planning and Scheduling. KE tools can be used to aid in the acquisition of knowledge and in the construction of domain models, which this book will illustrate. 

AI planning engines require a domain model which captures knowledge about how a particular domain works - e.g. the objects it contains and the available actions that can be used. However, encoding a planning domain model is not a straightforward task - a domain expert may be needed for their insight into the domain but this information must then be encoded in a suitable representation language. The development of such domain models is both time-consuming and error-prone. Due to these challenges, researchers have developed a number of automated tools and techniques to aid in the capture and representation of knowledge.

This book targets researchers and professionals working in knowledge engineering, artificial intelligence and software engineering. Advanced-level students studying AI will also be interested in this book.


Artificial Intelligence AI Planning & Scheduling Model-Based Reasoning Knowledge Engineering Knowledge Engineering Tools Knowledge based systems Computer Science Validation & Verification Knowledge Capture Knowledge Encoding

Editors and affiliations

  • Mauro Vallati
    • 1
  • Diane Kitchin
    • 2
  1. 1.Department of Computer ScienceUniversity of HuddersfieldHuddersfieldUK
  2. 2.Department of Computer ScienceUniversity of HuddersfieldHuddersfieldUK

Bibliographic information

  • DOI
  • Copyright Information Springer Nature Switzerland AG 2020
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-030-38560-6
  • Online ISBN 978-3-030-38561-3
  • Buy this book on publisher's site