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Normative design using inductive learning

Published online by Cambridge University Press:  06 July 2011

DOMENICO CORAPI ,
ALESSANDRA RUSSO ,
MARINA DE VOS ,
JULIAN PADGET  and
KEN SATOH
DOMENICO CORAPI
Affiliation:
Department of Computing, Imperial College London, 180 Queen's Gate, SW7 2AZ, London, UK (e-mail: d.corapi@ic.ac.uk, a.russo@ic.ac.uk)
ALESSANDRA RUSSO
Affiliation:
Department of Computing, Imperial College London, 180 Queen's Gate, SW7 2AZ, London, UK (e-mail: d.corapi@ic.ac.uk, a.russo@ic.ac.uk)
MARINA DE VOS
Affiliation:
Department of Computing, University of Bath, BA2 7AY, Bath, UK (e-mail: mdv@cs.bath.ac.uk, jap@cs.bath.ac.uk)
JULIAN PADGET
Affiliation:
Department of Computing, University of Bath, BA2 7AY, Bath, UK (e-mail: mdv@cs.bath.ac.uk, jap@cs.bath.ac.uk)
KEN SATOH
Affiliation:
Principles of Informatics Research Division, National Institute of Informatics, Chiyoda-ku, 2-1-2, Hitotsubashi, Tokyo 101-8430, Japan (e-mail: ksatoh@nii.ac.jp)
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Abstract

In this paper we propose a use-case-driven iterative design methodology for normative frameworks, also called virtual institutions, which are used to govern open systems. Our computational model represents the normative framework as a logic program under answer set semantics (ASP). By means of an inductive logic programming approach, implemented using ASP, it is possible to synthesise new rules and revise the existing ones. The learning mechanism is guided by the designer who describes the desired properties of the framework through use cases, comprising (i) event traces that capture possible scenarios, and (ii) a state that describes the desired outcome. The learning process then proposes additional rules, or changes to current rules, to satisfy the constraints expressed in the use cases. Thus, the contribution of this paper is a process for the elaboration and revision of a normative framework by means of a semi-automatic and iterative process driven from specifications of (un)desirable behaviour. The process integrates a novel and general methodology for theory revision based on ASP.

Keywords

normative frameworksinductive logic programmingtheory revision

Information

Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 11 , Special Issue 4-5: 27th International Conference on Logic Programming , July 2011 , pp. 783 - 799
Copyright
Copyright © Cambridge University Press 2011

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References

Alrajeh, D., Ray, O., Russo, A. and Uchitel, S. 2007. Extracting requirements from Scenarios using ILP. In Lecture Notes in Artificial Intelligence, Muggleton, S., Otera, R. P., and Tamaddoni-Nezhad, A., Eds. Vol. 4455, Springer-Verlag, New York, USA, 63–77.Google Scholar
Artikis, A. 2009. Dynamic protocols for open agent systems. In Proceedings of International Conference on Agents and Multi-Agent Systems (AAMAS), Decker, , Sichman, , Sierra, and Castelfranchi, , Eds., May, 10–15, 2009, Budapest, Hungary, 97–104.Google Scholar
Boella, G., Noriega, P., Pigozzi, G. and Verhagen, H., Eds. 2009a. Normative mult-agent systems, number 09121. In Dagstuhl Seminar Proceedings, Schloss Dagstuhl, Germany.Google Scholar
Boella, G., Pigozzi, G. and van der Torre, L. 2009b. Normative framework for normative system change. See Sierra et al., 169–176.Google Scholar
Boella, G., Pigozzi, G. and van der Torre, L. 2009c. Normative systems in computer science – ten guidelines for normative multiagent systems. See Boella et al. (2009a).Google Scholar
Cardoso, H. L. and Oliveira, E. C. 2008. Norm defeasibility in an institutional normative framework. In European Conference on Artificial Intelligence (ECAI), Ghallab, M., Spyropoulos, C. D., Fakotakis, N., and Avouris, N. M., Eds. Frontiers in Artificial Intelligence and Applications, Vol. 178, IOS, Virginia, USA, 468–472.Google Scholar
Christelis, G. and Rovatsos, M. 2009. Automated norm synthesis in an agent-based planning environment. See Sierra et al., 161–168.Google Scholar
Cliffe, O. 2007. Specifying and Analysing Institutions in Multi-Agent Systems Using Answer Set Programming. Ph.D. thesis, University of Bath, North East Somerset, UK.CrossRefGoogle Scholar
Cliffe, O., De Vos, M. and Padget, J. 2006. Answer set programming for representing and reasoning about virtual institutions. In Seventh International Workshop on Computational Logic in Multi-Agent Systems (CLIMA VII). Lecture Notes in Artificial Intelligence (LNAI), Vol. 4371. Springer, New York, USA, 60–79.Google Scholar
Corapi, D., Ray, O., Russo, A., Bandara, A. K. andLupu, E. C. 2009. Learning rules from user behaviour. In Artificial Intelligence Applications & Innovations (AIAI), Vol. 296, Springer, Boston, 459–468.Google Scholar
Corapi, D. and Russo, A. 2011. Aspal. Proof of Soundness and Completeness. Technical Report DTR11-5, Department of Computing, Imperial College, London.Google Scholar
Corapi, D., Russo, A. and Lupu, E. 2010. Inductive logic programming as abductive search. In Technical Communications of the 26th International Conference on Logic Programming, Hermenegildo, M. and Schaub, T., Eds. LIPICS, Vol. 7, Dagstuhl, Germany, 54–63.Google Scholar
Esteva, M., de la Cruz, D. and Sierra, C. 2002. Islander: An electronic institutions editor. In AAMAS. ACM, 1045–1052.Google Scholar
Fornara, N., Viganò, F., Verdicchio, M. and Colombetti, M. 2008. Artificial institutions: A model of institutional reality for open multiagent systems. Artificial Intelligence Law 16 (1), 89–105.Google Scholar
García-Camino, A., Rodríguez-Aguilar, J. A., Sierra, C. and Vasconcelos, W. W. 2009. Constraint rule-based programming of norms for electronic institutions. Autonomous Agents and Multi-Agent Systems 18 (1), 186–217.Google Scholar
García-Ojeda, J. C., DeLoach, S. A., Robby,Oyenan, W. H. and Valenzuela, J. 2007. O-mase: A customizable approach to developing multiagent development processes. In AOSE, Luck, M. and Padgham, L., Eds. Lecture Notes in Computer Science, Vol. 4951. Springer, New York, USA, 1–15.Google Scholar
Garion, C., Roussel, S. and Cholvy, L. 2009. A modal logic for reasoning on consistency and completeness of regulations. See Boella et al. (2009).Google Scholar
Gebser, M., Kaufmann, B., Neumann, A. and Schaub, T. 2007. clasp: A conflict-driven answer set solver. In LPNMR'07. Springer, New York, USA, 260–265.Google Scholar
Gelfond, M. and Lifschitz, V. 1991. Classical negation in logic programs and disjunctive databases. New Generation Computing 9 (3–4), 365–386.Google Scholar
Gelfond, M. and Lifschitz, V. 1998. Action languages. Electronic Transactions Artificial Intelligence 2, 193–210.Google Scholar
Hübner, J. F., Sichman, J. S. and Boissier, O. 2007. Developing organised multiagent systems using the moise. International Journal of Agent-Oriented Software Engineering 1 (3/4), 370–395.Google Scholar
Jones, A. J. and Sergot, M. 1996. A formal characterisation of institutionalised power. ACM Computing Surveys 28 (4es), 121. (Read 28/11/2004).Google Scholar
Kakas, A. C., Kowalski, R. A. and Toni, F. 1992. Abductive logic programming. Journal of Logic Computer 2 (6), 719–770.Google Scholar
Kollingbaum, M., Norman, T., Preece, A. and Sleeman, D. 2006. Norm conflicts and inconsistencies in virtual organisations. In Proceedings of COIN 2006, 245–258.Google Scholar
Kowalski, R. and Sergot, M. 1986. A logic-based calculus of events. New General Computer 4 (1), 67–95.Google Scholar
Muggleton, S. 1995. Inverse entailment and progol. New General Computer 13 (3&4), 245–286.CrossRefGoogle Scholar
Okouya, D. and Dignum, V. 2008. Operetta: A prototype tool for the design, analysis and development of multi-agent organizations. In AAMAS (Demos). IFAAMAS, 1677–1678.Google Scholar
Sakama, C. 2001a. Learning by answer sets. In Proceedings of the AAAI Spring Symposium on Answer Set Programming, 181–187, AAAI Press, California, USA.Google Scholar
Sakama, C. 2001b. Nonmonotonic inductive logic programming. In Proceedings of the 6th International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR 6). Notes in Artificial Intelligence 2173, Springer-Verlag, Berlin, Germany, 62–80.Google Scholar
Sattar, A. and Goebel, R. 1991. Using crucial literals to select better theories. Computational Intelligence 7, 11–22.Google Scholar
Savarimuthu, B. T. R. and Cranefield, S. 2009. A categorization of simulation works on norms. See Boella et al. (2009a).Google Scholar
Serrano, J.-M. and Saugar, S. 2009. Dealing with incomplete normative states. In Proceedings of COIN 2009, Padget, J. A., Artikis, A., Vasconcelos, W. W., Stathis, K., Torres da Silva, V., Matson, E. T., and Polleres, A., Eds. LNCS, Vol. 6069. Springer, New York, USA, 304–319.Google Scholar
Sierra, C., Castelfranchi, C., Decker, K. S. and Sichman, J. S., Eds. 2009. AAMAS 2009, Budapest, Hungary, May 10–15, 2009, Vol. 1. IFAAMAS.Google Scholar
Vasconcelos, W., Kollingbaum, M. and Norman, T. 2007. Resolving conflict and inconsistency in norm-regulated virtual organizations. In AAMAS, Durfee, E. H., Yokoo, M., Huhns, M. N., and Shehory, O., Eds. IFAAMAS, 91.Google Scholar
Wogulis, J. and Pazzani, M. J. 1993. A methodology for evaluating theory revision systems: Results with audrey ii. In International Joint Conference on Artificial Intelligence (IJCAI), 1128–1134.Google Scholar
Yamamoto, Y., Inoue, K. and Iwanuma, K. 2010. From inverse entailment to inverse subsumption. In 20th International Conference on Inductive Logic Programming (ILP), Firenze, Italy, June 27–30.Google Scholar