國立虎尾科技大學 |

Crime Modeling with Multi-Agent Learning.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Crime Modeling with Multi-Agent Learning./
作者:	Caskey, Trevor Ryan.
面頁冊數:	1 online resource (96 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
Contained By:	Dissertation Abstracts International79-04B(E).
標題:	Engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9780355564167

Crime Modeling with Multi-Agent Learning.
Caskey, Trevor Ryan.

Crime Modeling with Multi-Agent Learning. - 1 online resource (96 pages)

Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

This dissertation presents a formal game-theoretic belief learning approach to model criminology's Routine Activity Theory (RAT). RAT states that for a crime to occur a motivated offender (criminal) and a desirable target (victim) must meet in space and time without the presence of capable guardianship (law enforcement). The novelty in using belief learning to model the dynamics of RAT's offender, target, and guardian behaviors within an agent-based model is that the agents learn and adapt given observation of other agents' actions without knowledge of the payoffs that drove the other agents' choices. This is in contrast to other crime modeling research that has used reinforcement learning where the accumulated rewards gained from prior experiences are used to guide agent learning. This is an important distinction given the dynamics of RAT. It is the presence of the various agent types that provide opportunity for crime to occur, and not the potential for reward. Additionally, the belief learning approach presented fits the observed empirical data of case studies, producing statistically significant results with lower variance when compared to a reinforcement learning approach. Application of this new approach supports law enforcement in developing responses to crime problems and planning for the effects of displacement due to directed responses, thus deterring offenders and protecting the public through crime modeling with multi-agent learning.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355564167Subjects--Topical Terms:

561152
Engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Crime Modeling with Multi-Agent Learning.
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500 $a Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
500 $a Advisers: James S. Wasek; Anna Y. Franz.
502 $a Thesis (Ph.D.) $c The George Washington University $d 2018.
504 $a Includes bibliographical references
520 $a This dissertation presents a formal game-theoretic belief learning approach to model criminology's Routine Activity Theory (RAT). RAT states that for a crime to occur a motivated offender (criminal) and a desirable target (victim) must meet in space and time without the presence of capable guardianship (law enforcement). The novelty in using belief learning to model the dynamics of RAT's offender, target, and guardian behaviors within an agent-based model is that the agents learn and adapt given observation of other agents' actions without knowledge of the payoffs that drove the other agents' choices. This is in contrast to other crime modeling research that has used reinforcement learning where the accumulated rewards gained from prior experiences are used to guide agent learning. This is an important distinction given the dynamics of RAT. It is the presence of the various agent types that provide opportunity for crime to occur, and not the potential for reward. Additionally, the belief learning approach presented fits the observed empirical data of case studies, producing statistically significant results with lower variance when compared to a reinforcement learning approach. Application of this new approach supports law enforcement in developing responses to crime problems and planning for the effects of displacement due to directed responses, thus deterring offenders and protecting the public through crime modeling with multi-agent learning.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
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650 4 $a Systems science. $3 1148479
650 4 $a Criminology. $3 563146
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