Artificial Intelligence and Law

, Volume 20, Issue 2, pp 109–143 | Cite as

Argument diagram extraction from evidential Bayesian networks

  • Jeroen Keppens


Bayesian networks (BN) and argumentation diagrams (AD) are two predominant approaches to legal evidential reasoning, that are often treated as alternatives to one another. This paper argues that they are, instead, complimentary and proposes the beginnings of a method to employ them in such a manner. The Bayesian approach tends to be used as a means to analyse the findings of forensic scientists. As such, it constitutes a means to perform evidential reasoning. The design of Bayesian networks that accurately and comprehensively represent the relationships between investigative hypotheses and evidence remains difficult and sometimes contentious, however. Argumentation diagrams are representations of reasoning, and are used as a means to scrutinise reasoning (among other applications). In evidential reasoning, they tend to be used to represent and scrutinise the way humans reason about evidence. This paper examines how argumentation diagrams can be used to scrutinise Bayesian evidential reasoning by developing a method to extract argument diagrams from BN.


Evidential reasoning Bayesian reasoning  Argumentation 


  1. Aitken C, Taroni F, Garbolino P (2003) A graphical model for the evaluation of cross-transfer evidence in DNA profiles. Theor Popul Biol 63:179–190zbMATHCrossRefGoogle Scholar
  2. Bench-Capon T, Dunne P (2007) Argumentation in artificial intelligence. Artif Intell 171(10–15):619–641MathSciNetzbMATHCrossRefGoogle Scholar
  3. Bex F, van Koppen P, Prakken H, Verheij B (2010) A hybrid formal theory of arguments, stories and criminal evidence. Artif Intell Law 18(2):123–152CrossRefGoogle Scholar
  4. Biedermann A, Taroni F, Delemont O, Semadeni C, Davison A (2005) The evaluation of evidence in the forensic investigation of fire incidents. part ii. practical examples of the use of bayesian networks. Forensic Sci Int 147:59–69CrossRefGoogle Scholar
  5. Buckleton J, Triggs C, Champod C (2006) An extended likelihood ratio framework for interpreting evidence. Sci Justice 46(2):69–78CrossRefGoogle Scholar
  6. Condliffe P, Abrahams B, Zeleznikow J (2010) An OWL ontology and bayesian network to suport legal reasoning in the owners corporation domain. In: Proceedings of the 6th international workshop on online dispute resolution. pp 51–62Google Scholar
  7. Conway D (1991) On the distinction between convergent and linked arguments. Informal Log 13(3):145–158MathSciNetGoogle Scholar
  8. Cook R, Evett I, Jackson G, Jones P, Lambert J (1998) A model for case assessment and interpretation. Sci Justice 38(6):151–156CrossRefGoogle Scholar
  9. Corfield D, Williamson J (2001) Foundations of Bayesianism. Springer, BerlinzbMATHGoogle Scholar
  10. Davis G (2003) Bayesian reconstruction of traffic accidents. Law Probab Risk 2:69–89CrossRefGoogle Scholar
  11. Dawid A, Mortera J, Vicard P (2007) Object-oriented bayesian networks for complex forensic DNA profiling problems. Forensic Sci Int 169(2–3):195–205CrossRefGoogle Scholar
  12. de Campos L, Gámez J, Moral S (2001) Simplifying explanations in bayesian belief networks. Int J Uncertain Fuzziness Knowl Based Syst 9(4):461–489zbMATHCrossRefGoogle Scholar
  13. Druzdzel M, van der Gaag L (2000) Building probabilistic networks: where do the numbers come from?. IEEE Trans Knowl Data Eng 12(4):481–486CrossRefGoogle Scholar
  14. Dung P (1995) On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artif Intell 77(2):321–358MathSciNetzbMATHCrossRefGoogle Scholar
  15. Evett I, Jackson G, Lambert J, McCrossan S (2000) The impact of the principles of evidence interpretation on the structure and content of statements. Sci Justice 40(4):233–239CrossRefGoogle Scholar
  16. Gordon T, Prakken H, Walton D (2007) The carneades model of argument and burden of proof. Artif Intell 171(10–15):875–896MathSciNetzbMATHCrossRefGoogle Scholar
  17. Governatori G, Maher M, Antoniou G, Billington D (2004) Argumentation semantics for defeasible logic. J Log Comput 14(5):675–702MathSciNetzbMATHCrossRefGoogle Scholar
  18. Grabmair M, Gordon T, Walton D (2010) Probabilistic semantics for the carneades argument model using bayesian networks. In: Proceedings of the international conference on computational models of argument. IOS Press, Amsterdam, pp 255–266Google Scholar
  19. Green N (2011) Causal argumentation schemes to support sense-making in clinical genetics and law. In: Proceedings of the 13th international conference on artificial intelligence and law. pp 56–60Google Scholar
  20. Halpern J (2003) Reasoning about uncertainty. MIT Press, Cambridge, MAzbMATHGoogle Scholar
  21. Hepler A, Dawid P, Leucari V (2007) Object-oriented graphical representations of complex patterns of evidence. Law Probab Risk 6(1–4):275–293CrossRefGoogle Scholar
  22. Keppens J (2007) Towards qualitative approaches to bayesian evidential reasoning. In: Proceedings of the 11th international conference on artificial intelligence and law. pp 17–25Google Scholar
  23. Keppens J, Schafer B (2006) Knowledge based crime scenario modelling. Expert Syst Appl 30(2):203–222CrossRefGoogle Scholar
  24. Keppens J, Shen Q, Schafer B (2005) Probabilistic abductive computation of evidence collection strategies in crime investigation. In: Proceedings of the 10th international conference on artificial intelligence and law. pp 215–224Google Scholar
  25. Keppens J, Shen Q, Price C (2011) Compositional bayesian modelling for computation of evidence collection strategies. Appl Intell 35(1):134–161CrossRefGoogle Scholar
  26. Koller D, Pfeffer A (1997) Object-oriented bayesian networks. In: Proceedings of the 13th annual conference on uncertainty in artificial intelligence. pp 302–313Google Scholar
  27. Lacave C, Díez F (2002) A review of explanation methods for Bayesian networks. Knowl Eng Rev 17(2):107–127CrossRefGoogle Scholar
  28. Lacave C, Atienza R, Díez F (2000) Graphical explanation in bayesian networks. In: Proceedings 1st international symposium on medical data analysis. pp 122–129Google Scholar
  29. Laronge J (2009) A generalizable argument structure using defeasible class-inclusion transitivity for evaluating evidentiary probabive relevancy in litigation. J Log Comput. doi: 10.1093/logcom/exp066
  30. Mortera J, Dawid A, Lauritzen S (2003) Probabilistic expert systems for dna mixture profiling. Theor Popul Biol 63:191–205zbMATHCrossRefGoogle Scholar
  31. Parsons S (1997) Qualitative and quantitative practical reasoning, lecture notes in computer science, vol. 1244, chap. Normative argumentation and qualitative probability. Springer, Berlin, pp 466–480Google Scholar
  32. Pearl J (1988) Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, Los Altos, CAGoogle Scholar
  33. Prakken H, Reed C, Walton D (2005) Dialogues about the burden of proof. In: Proceedings of the 10th international conference on artificial intelligence and law. pp 115–124Google Scholar
  34. Reed C, Walton D, Macagno F (2007) Argument diagramming in logic, law and artificial intelligence. Knowl Eng Rev 22:87–109CrossRefGoogle Scholar
  35. Schum D (1994) The evidential foundations of probabilistic reasoning. Northwestern University Press, Evanston, ILGoogle Scholar
  36. Shimony S (1991) A probabilistic framework for explanation. PhD thesis, Brown University, Department of Computer ScienceGoogle Scholar
  37. Suermondt H (1992) Explanation in bayesian belief networks. PhD thesis, Stanford University, Department of Computer ScienceGoogle Scholar
  38. Thomas S (1986) Practical reasoning in natural language. Prentice-Hall, Englewood, NJGoogle Scholar
  39. Toulmin S (1958) The uses of argument. Cambridge University Press, CambridgeGoogle Scholar
  40. Walton D (2005) Argumentation methods for artificial intelligence in law. Springer, BerlinGoogle Scholar
  41. Wellman M, Henrion M (1993) Explaining "explaining away". IEEE Trans Pattern Anal Mach Intell 15:287–291CrossRefGoogle Scholar
  42. Wigmore J (1913) The principles of judicial proof. Little, Brown and Company, Boston, NYGoogle Scholar
  43. Yanal R (1991) Dependent and independent reasons. Informal Log 13(3):137–144Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.Department of InformaticsKing’s College LondonStrand, LondonUK

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