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Using observations to recognize the ...

Georgia Institute of Technology.

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Using observations to recognize the behavior of interacting multi-agent systems.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Using observations to recognize the behavior of interacting multi-agent systems./
作者:	Feldman, Adam.
面頁冊數:	163 p.
附註:	Adviser: Tucker Balch.
Contained By:	Dissertation Abstracts International69-09B.
標題:	Artificial Intelligence. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3327569
ISBN:	9780549801511

Using observations to recognize the behavior of interacting multi-agent systems.
Feldman, Adam.

Using observations to recognize the behavior of interacting multi-agent systems. - 163 p.

Adviser: Tucker Balch.

Thesis (Ph.D.)--Georgia Institute of Technology, 2008.

Behavioral research involves the study of the behaviors of one or more agents (often animals) in order to better understand the agents' thoughts and actions. Identifying subject movements and behaviors based upon those movements is a critical, time-consuming step in behavioral research. To successfully perform behavior analysis, three goals must be met. First, the agents of interest are observed, and their movements recorded. Second, each individual must be uniquely identified. Finally, behaviors must be identified and recognized. I explore a system that can uniquely identify and track agents, then use these tracks to automatically build behavioral models and recognize similar behaviors in the future.

ISBN: 9780549801511Subjects--Topical Terms:

769149
Artificial Intelligence.

Using observations to recognize the behavior of interacting multi-agent systems.
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100 1 $a Feldman, Adam. $3 1035829
245 1 0 $a Using observations to recognize the behavior of interacting multi-agent systems.
300 $a 163 p.
500 $a Adviser: Tucker Balch.
500 $a Source: Dissertation Abstracts International, Volume: 69-09, Section: B, page: 5515.
502 $a Thesis (Ph.D.)--Georgia Institute of Technology, 2008.
520 $a Behavioral research involves the study of the behaviors of one or more agents (often animals) in order to better understand the agents' thoughts and actions. Identifying subject movements and behaviors based upon those movements is a critical, time-consuming step in behavioral research. To successfully perform behavior analysis, three goals must be met. First, the agents of interest are observed, and their movements recorded. Second, each individual must be uniquely identified. Finally, behaviors must be identified and recognized. I explore a system that can uniquely identify and track agents, then use these tracks to automatically build behavioral models and recognize similar behaviors in the future.
520 $a I address the tracking and identification problems using a combination of laser range finders, active RFID sensors, and probabilistic models for real-time tracking. The laser range component adds environmental flexibility over vision based systems, while the RFID tags help disambiguate individual agents. The probabilistic models are important to target identification during the complex interactions with other agents of similar appearance. In addition to tracking, I present work on automatic methods for generating behavioral models based on supervised learning techniques using the agents' tracked data. These models can be used to classify new tracked data and identify the behavior exhibited by the agent, which can then be used to help automate behavior analysis.
590 $a School code: 0078.
650 4 $a Artificial Intelligence. $3 769149
650 4 $a Engineering, Robotics. $3 1018454
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710 2 $a Georgia Institute of Technology. $3 696730
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