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Modeling Purposeful Adaptive Behavio...

Ziebart, Brian D.

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Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy./
作者:	Ziebart, Brian D.
面頁冊數:	216 p.
附註:	Source: Dissertation Abstracts International, Volume: 72-02, Section: B, page: 0990.
Contained By:	Dissertation Abstracts International72-02B.
標題:	Engineering, Robotics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3438449
ISBN:	9781124414218

Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy.
Ziebart, Brian D.

Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy. - 216 p.

Source: Dissertation Abstracts International, Volume: 72-02, Section: B, page: 0990.

Thesis (Ph.D.)--Carnegie Mellon University, 2010.

Predicting human behavior from a small amount of training examples is a challenging machine learning problem. In this thesis, we introduce the principle of maximum causal entropy, a general technique for applying information theory to decision-theoretic, game-theoretic, and control settings where relevant information is sequentially revealed over time. This approach guarantees decision-theoretic performance by matching purposeful measures of behavior (Abbeel & Ng, 2004), and/or enforces game-theoretic rationality constraints (Aumann, 1974), while otherwise being as uncertain as possible, which minimizes worst-case predictive log-loss (Grunwald & Dawid, 2003).

ISBN: 9781124414218Subjects--Topical Terms:

1018454
Engineering, Robotics.

Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy.
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500 $a Adviser: J. Andrew Bagnell.
502 $a Thesis (Ph.D.)--Carnegie Mellon University, 2010.
520 $a Predicting human behavior from a small amount of training examples is a challenging machine learning problem. In this thesis, we introduce the principle of maximum causal entropy, a general technique for applying information theory to decision-theoretic, game-theoretic, and control settings where relevant information is sequentially revealed over time. This approach guarantees decision-theoretic performance by matching purposeful measures of behavior (Abbeel & Ng, 2004), and/or enforces game-theoretic rationality constraints (Aumann, 1974), while otherwise being as uncertain as possible, which minimizes worst-case predictive log-loss (Grunwald & Dawid, 2003).
520 $a We derive probabilistic models for decision, control, and multi-player game settings using this approach. We then develop corresponding algorithms for efficient inference that include relaxations of the Bellman equation (Bellman, 1957), and simple learning algorithms based on convex optimization. We apply the models and algorithms to a number of behavior prediction tasks. Specifically, we present empirical evaluations of the approach in the domains of vehicle route preference modeling using over 100,000 miles of collected taxi driving data, pedestrian motion modeling from weeks of indoor movement data, and robust prediction of game play in stochastic multi-player games.
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