Towards Practical Normative Agents: A Framework and an Implementation for Norm-Aware Planning

  • Sofia Panagiotidi
  • Javier Vázquez-Salceda
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7254)


Nowadays there is an important increase in the adoption and use of distributed computational solutions which are growing both in size (from tens to hundreds or even thousands of components, computational entities or actors) and in complexity (from closed, static, pre-defined interactions to more open, dynamic ones stablished at run-time). In this scenario one way to tame such complexity is to add a social layer on top regulating or shaping the behaviour of the actors in the system. One of those social abstractions that has been explored in literature is the use of computational models of (social or organisational) norms. Most of these approaches see norms as a way to specify acceptable agent behaviour in some (distributed) context. In literature there is a lot of work on norm theories, models and specifications on how agents might take norms into account when reasoning but few practical implementations. In this paper we present a first step into the implementation of practical normative agents by describing a framework and an implementation of norm-oriented planning. In this framework norms can be either obligations or prohibitions which can be violated, and are accompanied by repair norms in case they are breached. Unlike most frameworks, our approach takes into consideration the operationalisation of norms during the plan generation phase. Norm operational semantics is expressed as an extension/on top of STRIPS semantics, acting as a form of temporal restrictions over the trajectories (plans) computed by the planner. In combination with the agent’s utility functions over the actions, the norm-aware planner computes the most profitable trajectory concluding to a state of the world where no pending obligations exist and any (obligation/prohibition) violation has been handled. An implementation of the framework in PDDL is described.


Linear Temporal Logic Normative Reasoning Norm Violation Linear Temporal Logic Formula Hierarchical Task Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aldewereld, H.: Autonomy vs. conformity: An institutional perspective on norms and protocols. PhD Thesis, Utrecht University (2007),
  2. 2.
    Alvarez-Napagao, S., Aldewereld, H., Vázquez-Salceda, J., Dignum, F.: Normative Monitoring: Semantics and Implementation. In: De Vos, M., Fornara, N., Pitt, J.V., Vouros, G. (eds.) COIN 2010 International Workshops. LNCS, vol. 6541, pp. 321–336. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  3. 3.
    Astefanoaei, L., Dastani, M., Meyer, J.J., de Boer, F.S.: On the Semantics and Verification of Normative Multi-Agent Systems. International Journal of Universal Computer Science 15(13), 2629–2652 (2009)Google Scholar
  4. 4.
    Dastani, M., Tinnemeier, N.A., Meyer, J.J.: A Programming Language for Normative Multi-Agent Systems. In: Multi-Agent Systems: Semantics and Dynamics of Organizational Models, Hershey, PA, USA (2009)Google Scholar
  5. 5.
    Dignum, F.: Autonomous agents with norms. Artificial Intelligence and Law (7), 69–79 (1999),
  6. 6.
    Dignum, F., Broersen, J., Dignum, V., Meyer, J.-J.: Meeting the Deadline: Why, When and How. In: Hinchey, M.G., Rash, J.L., Truszkowski, W.F., Rouff, C.A. (eds.) FAABS 2004. LNCS (LNAI), vol. 3228, pp. 30–40. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Fikes, R., Nilsson, N.: STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving. Artificial Intelligence 2, 189–208 (1971)zbMATHCrossRefGoogle Scholar
  8. 8.
    Fornara, N., Colombetti, M.: Specifying and Enforcing Norms in Artificial Institutions. In: Baldoni, M., Son, T.C., van Riemsdijk, M.B., Winikoff, M. (eds.) DALT 2008. LNCS (LNAI), vol. 5397, pp. 1–17. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  9. 9.
    Fox, M., Long, D.: PDDL 2.1: An Extension to PDDL for Expressing Temporal Planning Domains, pp. 1–48. University of Durham, UK (2009)Google Scholar
  10. 10.
    Gerevini, A., Long, D.: Plan constraints and preferences in PDDL3: The Language of the Fifth International Planning Competition. Department of Electronics for Automation, University of Brescia, Italy (2006)Google Scholar
  11. 11.
    Ghallab, M., Nau, D., Traverso, P.: Automated Planning: Theory & Practice, 1st edn. The Morgan Kaufmann Series in Artificial Intelligence. Morgan Kaufmann (May 2004)Google Scholar
  12. 12.
    Hsu, C., Wah, B.: The sgplan planning system in ipc-6. In: 6th Int. Planning Competition Booklet, ICAPS 2008 (2008)Google Scholar
  13. 13.
    Kollingbaum, M.J.: Norm-governed practical reasoning agents. University of Aberdeen (January 2005)Google Scholar
  14. 14.
    Oren, N., Vasconcelos, W., Meneguzzi, F., Luck, M.: Acting on Norm Constrained Plans. In: Leite, J., Torroni, P., Ågotnes, T., Boella, G., van der Torre, L. (eds.) CLIMA XII 2011. LNCS, vol. 6814, pp. 347–363. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  15. 15.
    Oren, N., Panagiotidi, S., Vázquez-Salceda, J., Modgil, S., Luck, M., Miles, S.: Towards a Formalisation of Electronic Contracting Environments. In: Hübner, J.F., Matson, E., Boissier, O., Dignum, V. (eds.) COIN 2008. LNCS, vol. 5428, pp. 156–171. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  16. 16.
    Şensoy, M., Norman, T.J., Vasconcelos, W.W., Sycara, K.: OWL-POLAR: Semantic Policies for Agent Reasoning. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010, Part I. LNCS, vol. 6496, pp. 679–695. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  17. 17.
    Sergot, M.: The representation of law in computer programs: a survey and comparison, Tano. CompLex (Series), vol. 91(1), p. 99 (January 1991),
  18. 18.
    Sergot, M.: An action language for modelling norms and institutions. SIKS Masterclass, Utrecht (2003),
  19. 19.
    Sergot, M.J., Craven, R.: The Deontic Component of Action Language \(n{\mathcal{C}}+\). In: Goble, L., Meyer, J.-J.C. (eds.) DEON 2006. LNCS (LNAI), vol. 4048, pp. 222–237. Springer, Heidelberg (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Sofia Panagiotidi
    • 1
  • Javier Vázquez-Salceda
    • 1
  1. 1.Knowledge Engineering and Machine Learning GroupUniversitat Politècnica de Catalunya - BarcelonaTECH, Campus Nord UPC, Edifici K2MBarcelonaSpain

Personalised recommendations