Knowledge Engineering
Building Cognitive Assistants for Evidence-based Reasoning

Using robust software, this book focuses on learning assistants for evidence-based reasoning that learn complex problem solving from humans.

Gheorghe Tecuci (Author), Dorin Marcu (Author), Mihai Boicu (Author), David A. Schum (Author)

9781107122567, Cambridge University Press

Hardback, published 8 September 2016

480 pages, 350 colour illus. 59 tables
26.2 x 18.3 x 2.5 cm, 1.16 kg

'This well-written book is a much-needed update on the process of building expert systems. Gheorghe Tecuci and colleagues have developed the Disciple framework over many years and are using it here as a pedagogical tool for knowledge engineering. Hands-on exercises provide practical instruction to complement the explanations of principles, both of which make this a useful book for the classroom or self-study.' Bruce G. Buchanan, Emeritus Professor of Computer Science, University of Pittsburgh

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.

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.

Subject Areas: Expert systems / knowledge-based systems [UYQE], Artificial intelligence [UYQ], Engineering: general [TBC], Mathematical logic [PBCD], Knowledge management [KJMV3]