The Experts below are selected from a list of 29973 Experts worldwide ranked by ideXlab platform
Qiang Shen - One of the best experts on this subject based on the ideXlab platform.
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A scenario-driven decision support system for serious Crime Investigation
Law Probability and Risk, 2007Co-Authors: Qiang Shen, Jeroen Keppens, Colin Aitken, Burkhard Schafer, Mark LeeAbstract:Consideration of a wide range of plausible Crime scenarios during any Crime Investigation is important to seek convincing evidence and hence to minimize the likelihood of miscarriages of justice. It is equally important for Crime investigators to be able to employ effective and efficient evidence-collection strategies that are likely to produce the most conclusive information under limited available resources. An intelligent decision support system that can assist human investigators by automatically constructing plausible scenarios, and reasoning with the likely best investigating actions will clearly be very helpful in addressing these challenging problems. This paper presents a system for creating scenario spaces from given evidence, based on an integrated application of techniques for compositional modelling and Bayesian network-based evidence evaluation. Methods of analysis are also provided by the use of entropy to exploit the synthesized scenario spaces in order to prioritize investigating actions and hypotheses. These theoretical developments are illustrated by realistic examples of serious Crime Investigation.
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probabilistic abductive computation of evidence collection strategies in Crime Investigation
International Conference on Artificial Intelligence and Law, 2005Co-Authors: Jeroen Keppens, Qiang Shen, Burkhard SchaferAbstract:This paper presents a methodology for integrating two approaches to building decision support systems (DSS) for Crime Investigation: symbolic Crime scenario abduction [16] and Bayesian forensic evidence evaluation [5]. This is achieved by means of a novel compositional modelling technique that allows for automatically generating a space of models describing plausible Crime scenarios from given evidence and formally represented domain knowledge. The main benefit of this integration is that the resulting DSS is capable to formulate effective evidence collection strategies useful for differentiating competing Crime scenarios. A running example is used to demonstrate the theoretical developments.
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Compositional Bayesian modelling and its application to decision support in Crime Investigation
2005Co-Authors: Qiang Shen, Mark Lee, Jeroen KeppensAbstract:J. Keppens, Q. Shen and M. Lee. Compositional Bayesian modelling and its application to decision support in Crime Investigation. Proceedings of the 19th International Workshop on Qualitative Reasoning, pages 138-148.
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ICAIL - Probabilistic abductive computation of evidence collection strategies in Crime Investigation
Proceedings of the 10th international conference on Artificial intelligence and law - ICAIL '05, 2005Co-Authors: Jeroen Keppens, Qiang Shen, Burkhard SchaferAbstract:This paper presents a methodology for integrating two approaches to building decision support systems (DSS) for Crime Investigation: symbolic Crime scenario abduction [16] and Bayesian forensic evidence evaluation [5]. This is achieved by means of a novel compositional modelling technique that allows for automatically generating a space of models describing plausible Crime scenarios from given evidence and formally represented domain knowledge. The main benefit of this integration is that the resulting DSS is capable to formulate effective evidence collection strategies useful for differentiating competing Crime scenarios. A running example is used to demonstrate the theoretical developments.
Mark Lee - One of the best experts on this subject based on the ideXlab platform.
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A scenario-driven decision support system for serious Crime Investigation
Law Probability and Risk, 2007Co-Authors: Qiang Shen, Jeroen Keppens, Colin Aitken, Burkhard Schafer, Mark LeeAbstract:Consideration of a wide range of plausible Crime scenarios during any Crime Investigation is important to seek convincing evidence and hence to minimize the likelihood of miscarriages of justice. It is equally important for Crime investigators to be able to employ effective and efficient evidence-collection strategies that are likely to produce the most conclusive information under limited available resources. An intelligent decision support system that can assist human investigators by automatically constructing plausible scenarios, and reasoning with the likely best investigating actions will clearly be very helpful in addressing these challenging problems. This paper presents a system for creating scenario spaces from given evidence, based on an integrated application of techniques for compositional modelling and Bayesian network-based evidence evaluation. Methods of analysis are also provided by the use of entropy to exploit the synthesized scenario spaces in order to prioritize investigating actions and hypotheses. These theoretical developments are illustrated by realistic examples of serious Crime Investigation.
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Compositional Bayesian modelling and its application to decision support in Crime Investigation
2005Co-Authors: Qiang Shen, Mark Lee, Jeroen KeppensAbstract:J. Keppens, Q. Shen and M. Lee. Compositional Bayesian modelling and its application to decision support in Crime Investigation. Proceedings of the 19th International Workshop on Qualitative Reasoning, pages 138-148.
Jeroen Keppens - One of the best experts on this subject based on the ideXlab platform.
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A scenario-driven decision support system for serious Crime Investigation
Law Probability and Risk, 2007Co-Authors: Qiang Shen, Jeroen Keppens, Colin Aitken, Burkhard Schafer, Mark LeeAbstract:Consideration of a wide range of plausible Crime scenarios during any Crime Investigation is important to seek convincing evidence and hence to minimize the likelihood of miscarriages of justice. It is equally important for Crime investigators to be able to employ effective and efficient evidence-collection strategies that are likely to produce the most conclusive information under limited available resources. An intelligent decision support system that can assist human investigators by automatically constructing plausible scenarios, and reasoning with the likely best investigating actions will clearly be very helpful in addressing these challenging problems. This paper presents a system for creating scenario spaces from given evidence, based on an integrated application of techniques for compositional modelling and Bayesian network-based evidence evaluation. Methods of analysis are also provided by the use of entropy to exploit the synthesized scenario spaces in order to prioritize investigating actions and hypotheses. These theoretical developments are illustrated by realistic examples of serious Crime Investigation.
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probabilistic abductive computation of evidence collection strategies in Crime Investigation
International Conference on Artificial Intelligence and Law, 2005Co-Authors: Jeroen Keppens, Qiang Shen, Burkhard SchaferAbstract:This paper presents a methodology for integrating two approaches to building decision support systems (DSS) for Crime Investigation: symbolic Crime scenario abduction [16] and Bayesian forensic evidence evaluation [5]. This is achieved by means of a novel compositional modelling technique that allows for automatically generating a space of models describing plausible Crime scenarios from given evidence and formally represented domain knowledge. The main benefit of this integration is that the resulting DSS is capable to formulate effective evidence collection strategies useful for differentiating competing Crime scenarios. A running example is used to demonstrate the theoretical developments.
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Compositional Bayesian modelling and its application to decision support in Crime Investigation
2005Co-Authors: Qiang Shen, Mark Lee, Jeroen KeppensAbstract:J. Keppens, Q. Shen and M. Lee. Compositional Bayesian modelling and its application to decision support in Crime Investigation. Proceedings of the 19th International Workshop on Qualitative Reasoning, pages 138-148.
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ICAIL - Probabilistic abductive computation of evidence collection strategies in Crime Investigation
Proceedings of the 10th international conference on Artificial intelligence and law - ICAIL '05, 2005Co-Authors: Jeroen Keppens, Qiang Shen, Burkhard SchaferAbstract:This paper presents a methodology for integrating two approaches to building decision support systems (DSS) for Crime Investigation: symbolic Crime scenario abduction [16] and Bayesian forensic evidence evaluation [5]. This is achieved by means of a novel compositional modelling technique that allows for automatically generating a space of models describing plausible Crime scenarios from given evidence and formally represented domain knowledge. The main benefit of this integration is that the resulting DSS is capable to formulate effective evidence collection strategies useful for differentiating competing Crime scenarios. A running example is used to demonstrate the theoretical developments.
Andy Jones - One of the best experts on this subject based on the ideXlab platform.
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High-Technology Crime Investigation Program and Organization
High-Technology Crime Investigator's Handbook, 2006Co-Authors: Gerald Kovacich, Andy JonesAbstract:This chapter aims to describe and discuss the establishment and management of the organization chartered with the responsibility to lead the high-technology Crime prevention effort for GEC (a fictitious corporation), including describing the organization, structuring the organization, and detailing the job descriptions of the personnel to be hired to fill the positions within the high-technology Crime prevention organization. Establishing an effective and efficient high-technology Crime investigative unit and Crime prevention program requires a detailed analysis and integration of all the information that has been learned. Determining the need for a high-technology Crime investigative subordinate unit requires detailed analysis of company environment, budget, and an understanding of how to apply resource allocation techniques successfully to the high-technology Crime investigative and Crime prevention functions. After the need for a high-technology Crime prevention subordinate organization is determined, then the high-technology Crime investigator must determine what functions must be performed and in what priority.
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The High-Technology Crime Investigation Unit's Strategic, Tactical, and Annual Plans
High-Technology Crime Investigator's Handbook, 2006Co-Authors: Gerald Kovacich, Andy JonesAbstract:This chapter aims to establish the plans for the high-technology Crime prevention organization that provides the GEC (a fictitious corporation) security department's strategic, tactical, and annual plans, as well as those of GEC overall. These plans will set the direction for GEC's high-technology Crime prevention program (HTCPP), and their integration will indicate that the HTCPP is an integral part of both the GEC security department and GEC itself. When developing the high-technology Crime prevention strategic plan, the high-technology Crime investigator manager must ensure that the following basic high-technology Crime prevention principles are included, either specifically or in principle (because it is part of the high-technology Crime prevention strategy): 1. Minimize the probability of a high-technology Crime; 2. Conduct professional, effective, efficient, and discreet inquiries or Investigations if allegations of high-technology Crimes are reported; 3. Report the investigative findings to management (including an analysis of each Investigation), coordinate the results with other security functions, and make recommendations to minimize the probability of recurrence.
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Outsource or Proprietary
High-Technology Crime Investigator's Handbook, 2006Co-Authors: Gerald Kovacich, Andy JonesAbstract:This chapter discusses whether to outsource or keep in-house the high-technology Crime Investigation function. It should be noted that the high-technology Crime investigative function and unit is an overhead cost, as are all the GEC (the fictitious corporation) security department functions. Therefore, the CSO should always be analyzing the department's organizations and functions to determine whether they can be accomplished more effectively (better) and efficiently (cheaper). One way of meeting this objective is to look at outsourcing the high-technology Crime investigative function.
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High-Technology Crime Investigation Unit Metrics Management System
High-Technology Crime Investigator's Handbook, 2006Co-Authors: Gerald Kovacich, Andy JonesAbstract:Investigations and NCIs are security functions that, at some corporations, may be candidates for outsourcing. At this time, both are internal functions at the fictitious corporation GEC, and these two functions are much alike. However, the primary difference is scope and magnitude. That is to say, the term NCI is used to describe an Investigation that is conducted as a result of a violation of corporate policy or procedure, in which there has not been a violation of law. At GEC an Investigation is generally a much more complex process associated with a more serious situation and may involve a violation of the law or a regulation external to the corporation. This chapter addresses the use of security metrics as a tool for managing investigative and NCI security functions. Process analysis is discussed along with the use of metrics to assess the effectiveness, costs, benefits, successes, and failures.
Burkhard Schafer - One of the best experts on this subject based on the ideXlab platform.
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A scenario-driven decision support system for serious Crime Investigation
Law Probability and Risk, 2007Co-Authors: Qiang Shen, Jeroen Keppens, Colin Aitken, Burkhard Schafer, Mark LeeAbstract:Consideration of a wide range of plausible Crime scenarios during any Crime Investigation is important to seek convincing evidence and hence to minimize the likelihood of miscarriages of justice. It is equally important for Crime investigators to be able to employ effective and efficient evidence-collection strategies that are likely to produce the most conclusive information under limited available resources. An intelligent decision support system that can assist human investigators by automatically constructing plausible scenarios, and reasoning with the likely best investigating actions will clearly be very helpful in addressing these challenging problems. This paper presents a system for creating scenario spaces from given evidence, based on an integrated application of techniques for compositional modelling and Bayesian network-based evidence evaluation. Methods of analysis are also provided by the use of entropy to exploit the synthesized scenario spaces in order to prioritize investigating actions and hypotheses. These theoretical developments are illustrated by realistic examples of serious Crime Investigation.
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probabilistic abductive computation of evidence collection strategies in Crime Investigation
International Conference on Artificial Intelligence and Law, 2005Co-Authors: Jeroen Keppens, Qiang Shen, Burkhard SchaferAbstract:This paper presents a methodology for integrating two approaches to building decision support systems (DSS) for Crime Investigation: symbolic Crime scenario abduction [16] and Bayesian forensic evidence evaluation [5]. This is achieved by means of a novel compositional modelling technique that allows for automatically generating a space of models describing plausible Crime scenarios from given evidence and formally represented domain knowledge. The main benefit of this integration is that the resulting DSS is capable to formulate effective evidence collection strategies useful for differentiating competing Crime scenarios. A running example is used to demonstrate the theoretical developments.
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ICAIL - Probabilistic abductive computation of evidence collection strategies in Crime Investigation
Proceedings of the 10th international conference on Artificial intelligence and law - ICAIL '05, 2005Co-Authors: Jeroen Keppens, Qiang Shen, Burkhard SchaferAbstract:This paper presents a methodology for integrating two approaches to building decision support systems (DSS) for Crime Investigation: symbolic Crime scenario abduction [16] and Bayesian forensic evidence evaluation [5]. This is achieved by means of a novel compositional modelling technique that allows for automatically generating a space of models describing plausible Crime scenarios from given evidence and formally represented domain knowledge. The main benefit of this integration is that the resulting DSS is capable to formulate effective evidence collection strategies useful for differentiating competing Crime scenarios. A running example is used to demonstrate the theoretical developments.