Probabilistic abductive computation of evidence collection strategies in crime investigation

Authors:
Jeroen Keppens;Qiang Shen;Burkhard Schafer
Affiliations:
University of Wales, Aberystwyth, UK;University of Wales, Aberystwyth, UK;The University of Edinburgh, Edinburgh, UK
Venue:
ICAIL '05 Proceedings of the 10th international conference on Artificial intelligence and law
Year:
2005

Citing 14
Cited 3

An assumption-based TMS

Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
Explanation and prediction: an architecture for default and abductive reasoning

Computational Intelligence
Readings in model-based diagnosis

Readings in model-based diagnosis
Progress on Room 5: a testbed for public interactive semi-formal legal argumentation

Proceedings of the 6th international conference on Artificial intelligence and law
Automated argument assistance for lawyers

ICAIL '99 Proceedings of the 7th international conference on Artificial intelligence and law
Probability updating using second order probabilities and conditional event algebra

Information Sciences: an International Journal
Machine Learning

Machine Learning
Using Coplink to Analyze Criminal-Justice Data

Computer
Argumentation schemes and generalisations in reasoning about evidence

ICAIL '03 Proceedings of the 9th international conference on Artificial intelligence and law
Linguistic Bayesian Networks for reasoning with subjective probabilities in forensic statistics

ICAIL '03 Proceedings of the 9th international conference on Artificial intelligence and law
A model based reasoning approach for generating plausible crime scenarios from evidence

ICAIL '03 Proceedings of the 9th international conference on Artificial intelligence and law
Compositional model repositories via dynamic constraint satisfaction with order-of-magnitude preferences

Journal of Artificial Intelligence Research
Empirical analysis of case-based reasoning and other prediction methods in a social science domain: repeat criminal victimization

ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development

Information fusion for wireless sensor networks: Methods, models, and classifications

ACM Computing Surveys (CSUR)
Assumption Based Peg Unification for Crime Scenario Modelling

Proceedings of the 2005 conference on Legal Knowledge and Information Systems: JURIX 2005: The Eighteenth Annual Conference
Compositional Bayesian modelling for computation of evidence collection strategies

Applied Intelligence

Quantified Score

Hi-index	0.00

Visualization

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.