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Natalie Fridman, Gal A. Kaminka,
and Avishay Zilka. Towards Qualitative Reasoning for Policy Decision Support in Demonstrations. In Francien Dechesne,
Hiromitsu Hattori, Adriaan ter Mors, Jose M. Such, Danny Weyns, and Frank Dignum, editors, Advanced Agent Technology:
AAMAS 2011 Workshops. Revised Selected Papers, Lecture Notes in Computer Science (LNCS), pp. 19–34, Springer,
2012. Originally appeared in AMPLE 2011: First Workshop on Agent-based Modeling for Policy Engineering at AAMAS 2011
(unavailable)
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 what if scenarios. Thus, using QR approach is better for reasoning regarding the potential violence level to improve the police decision making process.
@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 = { },
}
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