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Effective decision-theoretic assista...

Natarajan, Sriraam.

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Effective decision-theoretic assistance through relational hierarchical models.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Effective decision-theoretic assistance through relational hierarchical models./
作者:	Natarajan, Sriraam.
面頁冊數:	167 p.
附註:	Source: Dissertation Abstracts International, Volume: 69-01, Section: B, page: 0431.
Contained By:	Dissertation Abstracts International69-01B.
標題:	Artificial Intelligence. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3295640
ISBN:	9780549405221

Effective decision-theoretic assistance through relational hierarchical models.
Natarajan, Sriraam.

Effective decision-theoretic assistance through relational hierarchical models. - 167 p.

Source: Dissertation Abstracts International, Volume: 69-01, Section: B, page: 0431.

Thesis (Ph.D.)--Oregon State University, 2007.

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.

ISBN: 9780549405221Subjects--Topical Terms:

769149
Artificial Intelligence.

Effective decision-theoretic assistance through relational hierarchical models.
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520 $a 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.
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