@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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@Article{tist13,
 author={Natalie Fridman and Gal A. Kaminka},
 title = {Using Qualitative Reasoning for Social Simulation of Crowds},
 journal = {{ACM} Transactions on Intelligent Systems and Technology},
 year = {2013},
OPTkey = {},
volume = {4},
number = {3},
pages = {54:1--54:21},
 OPTurl = {http://doi.acm.org/10.1145/2483669.2483687},
 OPTdoi = {10.1145/2483669.2483687},
 month = {June},
  wwwnote = {}, 
  OPTnote = {},
OPTannote = {},
 abstract =  {The ability to model and reason about the potential violence level of a demonstration is important to the 
  police decision making process. Unfortunately, existing knowledge regarding demonstrations is mostly distributed, without 
  a main source of data, and composed of partial qualitative descriptions without complete and precise numerical 
  information. In this paper we describe a first attempt to use qualitative reasoning techniques for this goal. To 
  our knowledge, such techniques have never been applied to modeling and reasoning regarding crowd behaviors, nor 
  in particular demonstrations. We develop qualitative models consistent with the partial, qualitative social 
  science literature, allowing us to model the interactions between different factors that influence violence 
  in demonstrations. We then utilize qualitative simulation to predict the potential eruption of violence, at 
  various levels, based on a description of the demographics, environmental settings, and police responses. We 
incrementally present and compare three such qualitative models. 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 good results. 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 what if scenarios. 
Moreover, we use the analysis algorithm to determine important factors in specific real-world demonstrations. We show that 
the algorithm identifies factors that correspond to experts analysis of these events.}
}