Knowledge engineering tools and techniques for AI planning

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.

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 Lear…