A scenario-driven decision support system for serious crime investigation

Shen, Qiang; Keppens, Jeroen; Aitken, Colin; Schäfer, Burkhard; Lee, Mark

doi:10.1093/lpr/mgl014

Cited by 37 publications

(33 citation statements)

References 21 publications

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“…In addition, the proposed novel linkbased similarity measure can be exploited for resolving identities and aggregating relevant scenarios in the environment of intelligence data analysis (Shen et al 2006). Specifically, as part of on-going work, the concepts of qualitative reasoning have been adopted to enhance a conventional numerical link analysis (that usually fails to achieve accurate and coherent interpretation of similarity measures).…”

Section: Resultsmentioning

confidence: 99%

Disclosing false identity through hybrid link analysis

2010

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Boongoen, T., Shen, Q., Price, C. J. (2010). Disclosing false identity through hybrid link analysis. Artificial Intelligence and Law, 18 (1), 77-102 Sponsorship: EPSRC RONO: EP/D057086/1Combating the identity problem is crucial and urgent as false identity has become a common denominator of many serious crimes, including mafia trafficking and terrorism. Without correct identification, it is very difficult for law enforcement authority to intervene, or even trace terrorists? activities. Amongst several identity attributes, personal names are commonly, and effortlessly, falsified or aliased by most criminals. Typical approaches to detecting the use of false identity rely on the similarity measure of textual and other content-based characteristics, which are usually not applicable in the case of highly deceptive, erroneous and unknown descriptions. This barrier can be overcome through analysis of link information displayed by the individual in communication behaviours, financial interactions and social networks. In particular, this paper presents a novel link-based approach that improves existing techniques by integrating multiple link properties in the process of similarity evaluation. It is utilised in a hybrid model that proficiently combines both text-based and link-based measures of examined names to refine the justification of their similarity. This approach is experimentally evaluated against other link-based and text-based techniques, over a terrorist-related dataset, with further generalization to a similar problem occurring in publication databases. The empirical study demonstrates the great potential of this work towards developing an effective identity verification system.Peer reviewe

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Section: Resultsmentioning

confidence: 99%

Disclosing false identity through hybrid link analysis

2010

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“…Inspired by this research, Vlek developed scenario idioms for the design of evidential Bayesian networks containing scenarios (Vlek et al 2014), and Timmer showed how argumentative information can be extracted from a Bayesian network (Timmer et al 2015b). Keppens and Schafer (2006) studied the knowledge-based generation of hypothetical scenarios for reasoning with evidence, later developed further in a decision support system (Shen et al 2006). This paper continues from an integrated perspective on arguments, scenarios and probabilities (Verheij 2014b).…”

Section: Discussionmentioning

confidence: 99%

Proof with and without probabilities

Verheij

2017

Artif Intell Law

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Evidential reasoning is hard, and errors can lead to miscarriages of justice with serious consequences. Analytic methods for the correct handling of evidence come in different styles, typically focusing on one of three tools: arguments, scenarios or probabilities. Recent research used Bayesian networks for connecting arguments, scenarios, and probabilities. Well-known issues with Bayesian networks were encountered: More numbers are needed than are available, and there is a risk of misinterpretation of the graph underlying the Bayesian network, for instance as a causal model. The formalism presented here models presumptive arguments about coherent hypotheses that are compared in terms of their strength. No choice is needed between qualitative or quantitative analytic styles, since the formalism can be interpreted with and without numbers. The formalism is applied to key concepts in argumentative, scenario and probabilistic analyses of evidential reasoning, and is illustrated with a fictional crime investigation example based on Alfred Hitchcock's film 'To Catch A Thief'.

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“…In particular, it has a major implication in the area of serious crime modelling and analysis. For example, in developing intelligent systems for intelligence data monitoring [1,21], an important trade-off needs to be considered: On the one hand, it is essential not to miss out any potentially significant scenarios that may later explain the observed evidence; on the other hand, too many unsorted and particularly, spurious scenarios may confuse human analysts. Thus, it is desirable to filter the created scenario space with respect to certain quality measures of the generated scenario descriptions.…”

Section: Discussionmentioning

confidence: 99%

“…Although different approaches exist for risk modelling, a general methodology is lacking for risk assessment of serious crime under fuzzy random circumstances [21]. In this paper, the occurrence of a serious crime is considered as a random event, while the loss incurred by the crime is considered as a fuzzy set.…”

Section: Introductionmentioning

confidence: 99%

Risk assessment of serious crime with fuzzy random theory

Shen

Zhao

2010

Information Sciences

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Q. Shen and R. Zhao. Risk assessment of serious crime with fuzzy random theory. Information Sciences, 180(22):4401-4411, 2010. Sponsorship: EPSRCThis paper presents a novel approach for assessing the potential risk of serious crime events (e.g. terrorist attack). The modelling and assessment of such risk is carried out under uncertain circumstances because of both the randomness and fuzziness inherent in crime data. The approach is based on fuzzy random theory that complements probability theory, with an additional dimension of imprecision. This allows for potential loss caused by a crime to be expressed as a fuzzy random variable. Crime risk is therefore estimated as the mean chance of a fuzzy random event, where the resulting loss reaches a given confidence level. The concept of the average loss per unit of time is also introduced, in order to calculate the rate at which the loss may increase due to possible crime events. The work is compared with typical existing approaches and supported with examples throughout that illustrate its utility.Peer reviewe

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A scenario-driven decision support system for serious crime investigation

Cited by 37 publications

References 21 publications

Disclosing false identity through hybrid link analysis

Disclosing false identity through hybrid link analysis

Proof with and without probabilities

Risk assessment of serious crime with fuzzy random theory

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