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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.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy./
Author:	Ziebart, Brian D.
Description:	216 p.
Notes:	Source: Dissertation Abstracts International, Volume: 72-02, Section: B, page: 0990.
Contained By:	Dissertation Abstracts International72-02B.
Subject:	Engineering, Robotics. -
Online resource:	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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020 $a 9781124414218
035 $a (UMI)AAI3438449
035 $a AAI3438449
040 $a UMI $c UMI
100 1 $a Ziebart, Brian D. $3 1680770
245 1 0 $a Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy.
300 $a 216 p.
500 $a Source: Dissertation Abstracts International, Volume: 72-02, Section: B, page: 0990.
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.
590 $a School code: 0041.
650 4 $a Engineering, Robotics. $3 1018454
650 4 $a Artificial Intelligence. $3 769149
650 4 $a Computer Science. $3 626642
690 $a 0771
690 $a 0800
690 $a 0984
710 2 $a Carnegie Mellon University. $3 1018096
773 0 $t Dissertation Abstracts International $g 72-02B.
790 1 0 $a Bagnell, J. Andrew, $e advisor
790 $a 0041
791 $a Ph.D.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3438449