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Artificial Intelligence and Law

, Volume 26, Issue 4, pp 315–344 | Cite as

RuleRS: a rule-based architecture for decision support systems

  • Mohammad Badiul IslamEmail author
  • Guido Governatori
Article

Abstract

Decision-makers in governments, enterprises, businesses and agencies or individuals, typically, make decisions according to various regulations, guidelines and policies based on existing records stored in various databases, in particular, relational databases. To assist decision-makers, an expert system, encompasses interactive computer-based systems or subsystems to support the decision-making process. Typically, most expert systems are built on top of transaction systems, databases, and data models and restricted in decision-making to the analysis, processing and presenting data and information, and they do not provide support for the normative layer. This paper will provide a solution to one specific problem that arises from this situation, namely the lack of tool/mechanism to demonstrate how an expert system is well-suited for supporting decision-making activities drawn from existing records and relevant legal requirements aligned existing records stored in various databases.We present a Rule-based (pre and post) reporting systems (RuleRS) architecture, which is intended to integrate databases, in particular, relational databases, with a logic-based reasoner and rule engine to assist in decision-making or create reports according to legal norms. We argue that the resulting RuleRS provides an efficient and flexible solution to the problem at hand using defeasible inference. To this end, we have also conducted empirical evaluations of RuleRS performance.

Keywords

Rule engine Defeasible logic Deontic Logic Decision support system SPINdle Legal norms 

Notes

Acknowledgements

Preliminary version of the material included in this paper appeared at ICAIL 2015 (Islam and Governatori 2015).

References

  1. Antoniou G, Billington D, Governatori G, Maher MJ (1999) On the modeling and analysis of regulations. In: Australian conference on information systemsGoogle Scholar
  2. Antoniou G, Billington D, Governatori G, Maher MJ (2001) Representation results for defeasible logic. ACM Trans Comput Logic 2(2):255–287MathSciNetCrossRefGoogle Scholar
  3. Basili VR (1996) The role of experimentation in software engineering: past, current, and future. In: Proceedings of the 18th ICSE, IEEE Computer Society, pp 442–449Google Scholar
  4. Billington D, Antoniou G, Governatori G, Maher MJ (2010) An inclusion theorem for Defeasible Logics. ACM Trans Comput Logic 12(1):1–27MathSciNetCrossRefGoogle Scholar
  5. Currim S, Snodgrass RT, Suh YK, Zhang R, Johnson MW, Yi C (2013) DBMS metrology: measuring query time. In: Proceedings of the 2013 ACM SIGMOD, ACM, pp 421–432Google Scholar
  6. Governatori G (2005) Representing business contracts in RuleML. Int J Coop Inf Syst 14(2–3):181–216CrossRefGoogle Scholar
  7. Governatori G (2011) On the relationship between Carneades and Defeasible Logic. In: Proceedings of the ICAIL 2011, ACM, pp 31–40Google Scholar
  8. Governatori G (2013) Business process compliance: an abstract normative framework. Inf Technol 55(6):231–238Google Scholar
  9. Governatori G (2015a) The Regorous approach to process compliance. In: 2015 IEEE 19th international enterprise distributed object computing workshop, IEEE Press, pp 33–40Google Scholar
  10. Governatori G (2015) Thou shalt is not you will. In: Atkinson K (ed) Proceedings of the fifteenth international conference on artificial intelligence and law. ACM, New York, pp 63–68Google Scholar
  11. Governatori G, Hashmi M (2015) No time for compliance. In: Enterprise distributed object computing conference (EDOC), 2015 IEEE 19th international, IEEE, pp 9–18Google Scholar
  12. Governatori G, Rotolo A (2004) Defeasible logic: agency, intention and obligation. In: Proceedings of the DEON 2004, Springer, vol 3065 in LNCS, pp 114–128Google Scholar
  13. Governatori G, Rotolo A (2006) Logic of violations: a Gentzen system for reasoning with contrary-to-duty obligations. Australas J Logic 4:193–215MathSciNetCrossRefGoogle Scholar
  14. Governatori G, Rotolo A (2008) BIO logical agents: norms, beliefs, intentions in defeasible logic. Auton Agent Multi-Agent Syst 17(1):36–69CrossRefGoogle Scholar
  15. Governatori G, Rotolo A (2010) A conceptually rich model of business process compliance. In: Proceedings of the APCCM 2010, ACS, vol 110 in CRPIT, pp 3–12Google Scholar
  16. Governatori G, Shek S (2013) Regorous: a business process compliance checker. In: Proceedings of the fourteenth international conference on artificial intelligence and law, pp 245–246Google Scholar
  17. Governatori G, Padmanabhan V, Rotolo A, Sattar A (2009) A defeasible logic for modelling policy-based intentions and motivational attitudes. Logic J IGPL 17(3):227–265MathSciNetCrossRefGoogle Scholar
  18. Governatori G, Olivieri F, Rotolo A, Scannapieco S (2013) Computing strong and weak permission in Defeasible Logic. J Philos Logic 42(6):799–829MathSciNetCrossRefGoogle Scholar
  19. Grosof BN (2004) Representing e-commerce rules via situated courteous logic programs in RuleML. Electron Commer Res Appl 3(1):2–20CrossRefGoogle Scholar
  20. Hashmi M, Governatori G, Wynn MT (2015) Normative requirements for regulatory compliance: an abstract formal framework. Inf Syst Front 18:429CrossRefGoogle Scholar
  21. Herrestad H (1991) Norms and formalization. In: Proceedings of the 3rd international conference on artificial intellilgence and law, ACM, pp 175–184Google Scholar
  22. Hu YJ, Yeh CL, Laun W (2009) Challenges for rule systems on the web. In: Governatori G, Hall J, Paschke A (eds) Rule interchange and applications. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 4–16Google Scholar
  23. International Business Machines Corporation (1993, 2001) Ibm db2 universal database sql reference version 8. http://bit.ly/IBMdb2s1e80, http://bit.ly/IBMdb2s2e80. Accessed 4 Apr 2016
  24. Islam MB, Governatori G (2015) Ruleoms: a rule-based online management system. In: Proceedings of the ICAIL 2015, ACM, New York, NY, USA, pp 187–191Google Scholar
  25. Kitchenham BA (1996) Evaluating software engineering methods and tool part 1: the evaluation context and evaluation methods. ACM SIGSOFT Notes 21(1):11–14CrossRefGoogle Scholar
  26. Kozák J (2011) Rules in database systems. In: Proceedings of contributed papers WDS’11, pp 131–136Google Scholar
  27. Lam HP, Governatori G (2009) The making of spindle. In: Governatori G, Hall J, Paschke A (eds) Rule interchange and applications. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 315–322 Google Scholar
  28. Liang S, Fodor P, Wan H, Kifer M (2009) Openrulebench: an analysis of the performance of rule engines. In: Proceedings of the 18th international conference on World wide web, ACM, pp 601–610Google Scholar
  29. Maher MJ (2001) Propositional defeasible logic has linear complexity. Theory Pract Logic Program 1(06):691–711MathSciNetCrossRefGoogle Scholar
  30. NSW Government (2013) Online mandatory reporter guide. http://sdm.community.nsw.gov.au/mrg/screen/DoCS/en-GB/summary?user=guest. Accessed 30 Sept 2014
  31. NSW Government (2016) The nsw mandatory reporter guide. http://www.keepthemsafe.nsw.gov.au/reporting_concerns/mandatory_reporter_guide. Accessed 15 June 2016
  32. Nute D (1994) Defeasible logic. In: Gabbay DM, Hogger CH, Robinson J (eds) Handbook of logic in artificial intelligence and logic programming, vol 3. Oxford University Press, Oxford, pp 353–395Google Scholar
  33. Osman R, Knottenbelt W (2012) Database system performance evaluation models: a survey. Perform Eval 69:471–493CrossRefGoogle Scholar
  34. Paton NW, Díaz O (1999) Active database systems. ACM Comput Surv CSUR 31(1):63–103CrossRefGoogle Scholar
  35. Sadiq S, Governatori G (2015) Managing regulatory compliance in business processes. In: vom Brocke J, Rosemann M (eds) Handbook of business process management, vol 2, 2nd edn. Springer, Berlin, pp 265–288Google Scholar
  36. Skylogiannis T, Antoniou G, Bassiliades N, Governatori G, Bikakis A (2007) Dr-negotiate—a system for automated agent negotiation with defeasible logic-based strategies. Data Knowl Eng 63:362–380CrossRefGoogle Scholar
  37. The PostgreSQL Global Development Group (1996–2015) Postgresql 9.4.4 documentation. http://www.postgresql.org/files/documentation/pdf/9.4/postgresql-9.4-A4.pdf. Accessed 15 Nov 2015
  38. US Food and Drug Administration (2015) FDA adverse event reporting system (FAERS): latest quarterly data files. http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Surveillance/AdverseDrugEffects/ucm082193.htm. Accessed 15 Mar 2016
  39. US Food and Drug Administration (2016) FDA adverse event reporting system (FAERS). https://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Surveillance/AdverseDrugEffects/. Accessed 16 Feb 2018
  40. US Government (2014) Records and reports concerning adverse drug experiences on marketed prescription drugs for human use without approved new drug applications. http://bit.ly/eCFR310_305. Accessed 7 Mar 2016
  41. Viktoratos I, Tsadiras A, Bassiliades N (2012) Plis+: a rule-based personalized location information system. In: Proceedings of the RuleML2012@ECAI challenge, vol 874 in CEUR workshop proceedingsGoogle Scholar
  42. Yu P, Chen M, Heiss H (1992) On workload characterization of relational database environments. IEEE Trans Softw Eng 18:347–355CrossRefGoogle Scholar
  43. Zelkowitz MV, Wallace DR (1998) Experimental models for validating technology. Computer 31(5):23–31CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Data61, CSIROBrisbaneAustralia

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