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Intelligent Assistants - A Decision-Theoretic Model

Sriraam Natarajan

ISBN 10: 3639145801 / ISBN 13: 9783639145809
Published by VDM Verlag
New Condition: New Soft cover
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Paperback. 176 pages. Dimensions: 8.7in. x 5.9in. x 0.4in.Building intelligent computer assistants has been a long-cherished goal of AI. Many intelligent assistant systems were built and fine-tuned to specific application domains. In this work, we develop a general model of assistance that combines three powerful ideas: decision theory, hierarchical task models and probabilistic relational languages. We use the principles of decision theory to model the general problem of intelligent assistance. We use a combination of hierarchical task models and probabilistic relational languages to specify prior knowledge of the computer assistant. The assistant exploits its prior knowledge to infer the users goals and takes actions to assist the user. We evaluate the decision theoretic assistance model in three different domains including a real-world domain to demonstrate its generality. We show through experiments that both the hierarchical structure of the goals and the parameter sharing facilitated by relational models significantly improve the learning speed of the agent. Finally, we present the results of deploying our relational hierarchical model in a real-world activity recognition task. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Bookseller Inventory # 9783639145809

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Bibliographic Details

Title: Intelligent Assistants - A ...

Publisher: VDM Verlag

Binding: Paperback

Book Condition:New

Book Type: Paperback

About this title

Synopsis:

Building intelligent computer assistants has been a long-cherished goal of AI. Many intelligent assistant systems were built and fine-tuned to specific application domains. In this work, we develop a general model of assistance that combines three powerful ideas: decision theory, hierarchical task models and probabilistic relational languages. We use the principles of decision theory to model the general problem of intelligent assistance. We use a combination of hierarchical task models and probabilistic relational languages to specify prior knowledge of the computer assistant. The assistant exploits its prior knowledge to infer the user's goals and takes actions to assist the user. We evaluate the decision theoretic assistance model in three different domains including a real-world domain to demonstrate its generality. We show through experiments that both the hierarchical structure of the goals and the parameter sharing facilitated by relational models significantly improve the learning speed of the agent. Finally, we present the results of deploying our relational hierarchical model in a real-world activity recognition task.

About the Author:

Sriraam Natarajan is currently a Post-Doctoral Research Associate at the University of Wisconsin, Madison. He completed his Ph.D at Oregon State University working with Dr.Prasad Tadepalli. His research interests lie in the fields of Statistical Relational learning, Graphical Models, Reinforcement Learning, User Modeling and Planning.

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