Article

Probabilistic abductive computation of evidence collection strategies in crime investigation

Authors:

Jeroen Keppens,

Burkhard SchaferAuthors Info & Claims

ICAIL '05: Proceedings of the 10th international conference on Artificial intelligence and law

Pages 215 - 224

https://doi.org/10.1145/1165485.1165520

Published: 06 June 2005 Publication History

Abstract

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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Cited By

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https://doi.org/10.1371/journal.pone.0316317
Saari EJantan A(2013)E-Cyborg: The cybercrime evidence finder2013 8th International Conference on Information Technology in Asia (CITA)10.1109/CITA.2013.6637579(1-6)Online publication date: Jul-2013
https://doi.org/10.1109/CITA.2013.6637579
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https://doi.org/10.4018/978-1-61350-513-7.ch006
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cover image ACM Other conferences

ICAIL '05: Proceedings of the 10th international conference on Artificial intelligence and law

June 2005

270 pages

ISBN:1595930817

DOI:10.1145/1165485

Conference Chair:
Giovanni Sartor
CIRSFID - Universita di Bologna, Italy

Copyright © 2005 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 06 June 2005

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ICAIL05: 10th International Joint Conference on Artificial Intellignece and Law

June 6 - 11, 2005

Bologna, Italy

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Cited By

Sun YPang HJärvilehto LZhang OShapiro DKorkman JHaginoya SSanttila P(2025)Comparing the performance of a large language model and naive human interviewers in interviewing children about a witnessed mock-eventPLOS ONE10.1371/journal.pone.031631720:2(e0316317)Online publication date: 28-Feb-2025
https://doi.org/10.1371/journal.pone.0316317
Saari EJantan A(2013)E-Cyborg: The cybercrime evidence finder2013 8th International Conference on Information Technology in Asia (CITA)10.1109/CITA.2013.6637579(1-6)Online publication date: Jul-2013
https://doi.org/10.1109/CITA.2013.6637579
Shen QBoongoen T(2012)Social Network Inspired Approach to Intelligent Monitoring of Intelligence DataSocial Network Mining, Analysis, and Research Trends10.4018/978-1-61350-513-7.ch006(79-100)Online publication date: 2012
https://doi.org/10.4018/978-1-61350-513-7.ch006
Keppens J(2012)Argument diagram extraction from evidential Bayesian networksArtificial Intelligence and Law10.1007/s10506-012-9121-z20:2(109-143)Online publication date: 1-May-2012
https://dl.acm.org/doi/10.1007/s10506-012-9121-z
Tse HChow KKwan M(2012)Reasoning about Evidence using Bayesian NetworksAdvances in Digital Forensics VIII10.1007/978-3-642-33962-2_7(99-113)Online publication date: 2012
https://doi.org/10.1007/978-3-642-33962-2_7
Keppens JShen QPrice C(2011)Compositional Bayesian modelling for computation of evidence collection strategiesApplied Intelligence10.1007/s10489-009-0208-535:1(134-161)Online publication date: 1-Aug-2011
https://dl.acm.org/doi/10.1007/s10489-009-0208-5
Nissan ENissan E(2011)The Forensic Disciplines: Some Areas of Actual or Potential ApplicationComputer Applications for Handling Legal Evidence, Police Investigation and Case Argumentation10.1007/978-90-481-8990-8_8(841-989)Online publication date: 24-Oct-2011
https://doi.org/10.1007/978-90-481-8990-8_8
Nissan ENissan E(2011)Models of Forming an OpinionComputer Applications for Handling Legal Evidence, Police Investigation and Case Argumentation10.1007/978-90-481-8990-8_2(13-128)Online publication date: 24-Oct-2011
https://doi.org/10.1007/978-90-481-8990-8_2
Saari EJantan A(2011)F-IDS: A Technique for Simplifying Evidence Collection in Network ForensicsSoftware Engineering and Computer Systems10.1007/978-3-642-22203-0_58(693-701)Online publication date: 2011
https://doi.org/10.1007/978-3-642-22203-0_58
Noor NSaman MGhazali AZainuddin Z(2010)An integrated DNA decision support system using grid environment for crime investigation2010 International Symposium on Information Technology10.1109/ITSIM.2010.5561529(636-641)Online publication date: Jun-2010
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