@COMMENT This file was generated by bib2html.pl <https://sourceforge.net/projects/bib2html/> version 0.94
@COMMENT written by Patrick Riley <http://sourceforge.net/users/patstg/>
@COMMENT This file came from Gal A. Kaminka's publication pages at
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@InCollection{qual-aamasworkshops11,
author = {Natalie Fridman and Gal A. Kaminka and Avishay Zilka},
title = {Towards Qualitative Reasoning for Policy Decision Support in Demonstrations},
booktitle = {Advanced Agent Technology:  AAMAS 2011 Workshops. Revised Selected Papers},
OPTcrossref = {},
OPTkey = {},
pages = {19--34},
year = {2012},
editor = {Francien Dechesne and Hiromitsu Hattori and Adriaan ter Mors and Jose M. Such and Danny Weyns and Frank Dignum},
volume = {7068},
OPTnumber = {},
series = {Lecture Notes in Computer Science ({LNCS})},
OPTaddress = {},
OPTmonth = {},
OPTorganization = {},
publisher = {Springer},
note = 	 {Originally appeared in {AMPLE} 2011: First Workshop on Agent-based Modeling for Policy Engineering at {AAMAS} 2011}, 
OPTannote = {},
OPTurl = {http://dx.doi.org/10.1007/978-3-642-27216-5_3},
OPTdoi = {},
OPTissn = {978-3-642-27215-8},
OPTlocalfile = {},
abstract = {In this paper we describe a method for modeling social behavior of large groups, and apply it to the problem of predicting  potential violence during demonstrations. We use qualitative reasoning techniques which to our knowledge have never been applied to modeling  crowd behaviors, nor in particular to demonstrations. Such modeling may not only contribute to the police decision making process, but can also  provide a great opportunity to test existing theories in social science. We incrementally present and compare three qualitative models, based  on social science theories. The results show that while two of these models fail to predict the outcomes of real-world events reported and  analyzed in the literature, one model provide a good results. Moreover, in this paper we examine whether machine learning techniques such as  decision trees may provide better predictions than QR models. While the results show that the machine learning techniques provide accurate  predictions, a slightly better prediction than our QR model, we claim that QR approach is sensitive to changes in contrast to decision tree,  and can account for \emph{what if} scenarios. Thus, using QR approach is better for reasoning regarding the potential violence level to improve  the police decision making process.},
  wwwnote = { }, 
}