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This book presents a significant advancement in the theory and practice of knowledge engineering, the discipline concerned with the development of intelligent agents that use knowledge and reasoning to perform problem solving and decision-making tasks. It covers the main stages in the development of a knowledge-based agent: understanding the application domain, modeling problem solving in that domain, developing the ontology, learning the reasoning rules, and testing the agent. The book focuses on a special class of agents: cognitive assistants for evidence-based reasoning that learn complex problem-solving expertise directly from human experts, support experts, and nonexperts in problem solving and decision making, and teach their problem-solving expertise to students. A powerful learning agent shell, Disciple-EBR, is included with the book, enabling students, practitioners, and researchers to develop cognitive assistants rapidly in a wide variety of domains that require evidence-based reasoning, including intelligence analysis, cybersecurity, law, forensics, medicine, and education.

Presents a significant advancement in the theory and practice of knowledge engineering
Follows a hands-on approach to learning knowledge engineering
Disciple-EBR is provided as a tool to develop personal learning assistants



Table of Contents

1. Introduction
2. Evidence-based reasoning: connecting the dots
3. Methodologies and tools for agent design and development
4. Modeling the problem-solving process
5. Ontologies
6. Ontology design and development
7. Reasoning with ontologies and rules
8. Learning for knowledge-based agents
9. Rule learning
10. Rule refinement
11. Abstraction of reasoning
12. Disciple agents
13. Design principles for cognitive assistants.