Knowledge engineering tools and techniques for AI planning

Knowledge engineering tools and techniques for AI planning / Mauro Vallati, Diane Kitchin, editors.

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 plan...

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Bibliographic Details
Other Authors: Vallati, Mauro, Kitchin, Diane
Format: eBook
Language:English
Published: Cham : Springer, 2020.
Subjects:
Artificial intelligence.
artificial intelligence.
Knowledge management.
Data mining.
Expert systems > knowledge-based systems.
Business & Economics > Information Management.
Computers > Database Management > Data Mining.
Computers > Expert Systems.
Artificial intelligence
Online Access:Click for online access
Table of Contents:
  • Intro
  • Preface
  • Contents
  • Part I Knowledge Capture and Encoding
  • 1 Explanation-Based Learning of Action Models
  • 1 Introduction
  • 2 Background
  • 2.1 Classical Planning with Conditional Effects
  • 2.2 The Observation Model
  • 2.3 Explaining Observations with Classical Planning
  • 3 Explanation-Based Learning of Strips Action Models
  • 3.1 The Space of Strips Action Models
  • 3.2 The Sampling Space
  • 4 Learning Strips Action Models with Classical Planning
  • 4.1 Compilation
  • 4.2 Properties of the Compilation
  • 5 Experimental Results
  • 5.1 Learning from Labeled Plans
  • 5.2 Learning from Initial/Final State Pairs
  • 6 Conclusions
  • References
  • 2 Automated Domain Model Learning Tools for Planning
  • 1 Introduction
  • 1.1 Knowledge Representation for Knowledge Engineering of Domain Models
  • 2 Domain Model Learning Techniques and Tools
  • 2.1 Inductive Learning
  • 2.1.1 When to Use Inductive Learning
  • 2.2 Knowledge-Based Inductive Learning (KBIL)
  • 2.3 Analytical Learning
  • 2.4 Hybrid Learning
  • 2.5 Surprise-Based Learning (SBL)
  • 2.6 Transfer Learning
  • 2.7 Policy Learning
  • 2.8 Other Methods of Knowledge Acquisition
  • 3 Characteristics of the Domain Model Learning Tools
  • 4 Conclusion
  • References
  • 3 Formal Knowledge Engineering for Planning: Pre and Post-Design Analysis
  • 1 Introduction
  • 2 Knowledge Engineering and Planning
  • 3 Domain Modeling in AI Planning
  • 3.1 Accuracy
  • 3.2 Adequacy
  • 3.3 Operationality
  • 4 A Knowledge Engineering Design Approach for Planning
  • 5 PDM and Post-Design Modeling Using Petri Nets
  • 6 New Perspectives for AI Planning in Automation Systems
  • References
  • 4 MyPDDL: Tools for Efficiently Creating PDDL Domains and Problems
  • 1 Introduction
  • 2 Related Work
  • 2.1 Critical Review
  • 3 MyPDDL
  • 3.1 Modules
  • 4 Validation and Evaluation
  • 4.1 User Evaluation
  • 4.1.1 Analysis
  • 4.1.2 Results
  • 5 Conclusion
  • Appendix: Tasks
  • Deliberately Erroneous Logistics Domain
  • Deliberately Erroneous Coffee Domain
  • Planet Splisus
  • Store
  • References
  • 5 KEPS Book: Planning. Domains
  • 1 Planning. Domains Solver
  • 1.1 Libraries
  • 1.2 API Future
  • 2 Solver Planning Domains
  • 2.1 Solver Future
  • 3 Editor Planning Domains
  • 3.1 Plugin Framework
  • 3.2 Session Functionality
  • 3.3 Editor Future
  • 4 Education Planning Domains
  • 5 What Is Next for Planning. Domains
  • 5.1 Planimation
  • 5.2 VSCode Integration
  • 6 Conclusion
  • References
  • 6 Modeling Planning Tasks: Representation Matters
  • 1 Introduction
  • 2 Outer Entanglements
  • 3 Macro-Operators
  • 4 Bagged Representation
  • 5 Procedural Domain Control Knowledge
  • 6 Transition-Based Domain Control Knowledge
  • 7 A Case Study: The Spanner Domain
  • 8 Conclusion
  • References
  • Part II Interaction, Visualisation, and Explanation
  • 7 An Interactive Tool for Plan Generation, Inspection, and Visualization
  • 1 Introduction
  • 2 Preliminaries

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