Building Intelligent Agents is unique in its comprehensive coverage of the subject. The first part of the book presents an original theory for building intelligent agents and a methodology and tool that implement the theory. The second part of the book presents complex and detailed case studies of building different types of agents: an educational assessment agent, a statistical analysis assessment and support agent, an engineering design assistant, and a virtual military commander. Also featured in this book is Disciple, a toolkit for building interactive agents which function in much the same way as a human apprentice. Disciple-based agents can reason both with incomplete information, but also with information that is potentially incorrect. This approach, in which the agent learns its behavior from its teacher, integrates many machine learning and knowledge acquisition techniques, taking advantage of their complementary strengths to compensate for each others weakness. As a consequence, it significantly reduces (or even eliminates) the involvement of a knowledge engineer in the process of building an intelligent agent.
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About the Author
Gheorghe Tecuci is Professor of Computer Science at George Mason University and a full member of the Romanian Academy. He has had around 100 scientific papers published, and is best known for his contributions to multistrategy learning and integrated machine learning and knowledge acquisition.
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Inside This Book
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First Sentence:
This chapter presents the definition of an intelligent agent and discusses some of the general issues in developing such an agent. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
plausible lower bound condition, plausible upper bound condition, first memory bank, maximally general specialization, mean petal length, iris verginica, knowledge base development process, rule refinement process, statistical analysis assessment, plausible version space, known positive examples, loudspeaker manufacturing, slave field work, building intelligent agents, association browser, knowledge acquisition research, manufacturing assistant, knowledge base refinement, cigarette data, knowledge base manager, relevancy rules, disciple approach, concept browser, multistrategy approach, multistrategy learning Key Phrases - Capitalized Phrases (CAPs): (learn more)
Library of Congress, Southern Illustrated News, Generate Edit, The Natural World, Fixed Variables, Variable Filter, Accepted Examples, Christian Recorder, Cultivating Sugar, Distinct Variables, Explanation Groups, Object Hierarchy, Disciple Example, Current Example Previous Example, Rule View, Civil Var, Done Figure, Elements Filter, Gheorghe Tecuci, Name Description Domain Range, Tomasz Dybala, World Wide Web, African American, Plausible Louver Bound, Properties Values New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
This chapter presents the definition of an intelligent agent and discusses some of the general issues in developing such an agent. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
plausible lower bound condition, plausible upper bound condition, first memory bank, maximally general specialization, mean petal length, iris verginica, knowledge base development process, rule refinement process, statistical analysis assessment, plausible version space, known positive examples, loudspeaker manufacturing, slave field work, building intelligent agents, association browser, knowledge acquisition research, manufacturing assistant, knowledge base refinement, cigarette data, knowledge base manager, relevancy rules, disciple approach, concept browser, multistrategy approach, multistrategy learning Key Phrases - Capitalized Phrases (CAPs): (learn more)
Library of Congress, Southern Illustrated News, Generate Edit, The Natural World, Fixed Variables, Variable Filter, Accepted Examples, Christian Recorder, Cultivating Sugar, Distinct Variables, Explanation Groups, Object Hierarchy, Disciple Example, Current Example Previous Example, Rule View, Civil Var, Done Figure, Elements Filter, Gheorghe Tecuci, Name Description Domain Range, Tomasz Dybala, World Wide Web, African American, Plausible Louver Bound, Properties Values New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 66
books:
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- Machine Learning: An Artificial Intelligence Approach (Volume I) by Ryszard S. Michalski on 13 pages
- Machine Learning by Tom M. Mitchell on 9 pages
- Machine Learning and Knowledge Acquisition: Integrated Approaches (Knowledge-Based Systems) by Gheorghe Tecuci on 8 pages
- Software Agents by Jeffrey M. Bradshaw on 7 pages
- Multistrategy Learning (The Springer International Series in Engineering and Computer Science) by Ryszard S. Michalski on 5 pages
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