Gheorghe Tecuci

Knowledge Engineering (eBook, PDF)

Building Cognitive Assistants for Evidence-based Reasoning

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 8. September 2016
• Englisch
• ISBN-13: 9781316655580
• Artikelnr.: 52954320

Autorenporträt

Gheorghe Tecuci (PhD, University of Paris-South and Polytechnic Institute of Bucharest) is Professor of Computer Science and Director of the Learning Agents Center at George Mason University, Virginia, Member of the Romanian Academy, and former Chair of Artificial Intelligence at the US Army War College. He has published 11 books and more than 190 papers.

Inhaltsangabe

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.