Integrating CP-Nets in Reactive BDI Agents

  • Mostafa Mohajeri PariziEmail author
  • Giovanni SilenoEmail author
  • Tom van EngersEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11873)


Computational agents based upon the belief-desire-intention (BDI) architecture generally use reactive rules to trigger the execution of plans. For various reasons, certain plans might be preferred over others at design time. Most BDI agents platforms use hard-coding these preferences in some form of the static ordering of the reactive rules, but keeping the preferential structure implicit limits script reuse and generalization. This paper proposes an approach to add qualitative preferences over adoption/avoidance of procedural goals into an agent script, building upon the well-known notation of conditional ceteris paribus preference networks (CP-nets). For effective execution, the procedural knowledge and the preferential structure of the agent are mapped in an off-line fashion into a new reactive agent script. This solution contrasts with recent proposals integrating preferences as a rationale in the decision making cycle, and so overriding the reactive nature of BDI agents.


BDI agents Conditional preferences Procedural goals Goal adoption/avoidance CP-Nets Reactive agents 



This paper results from work done within the NWO-funded project Data Logistics for Logistics Data (DL4LD,, supported by the Dutch Top consortia for Knowledge and Innovation Institute for Advanced Logistics (TKI Dinalog, of the Ministry of Economy and Environment in The Netherlands and the Commit-to-Data initiative (, and partly within the NWO-funded program VWDATA.


  1. 1.
    Aschermann, M., Dennisen, S., Kraus, P., Müller, J.P.: LightJason, a highly scalable and concurrent agent framework: overview and application. In: Proceedings of the 17th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2018), pp. 1794–1796 (2018)Google Scholar
  2. 2.
    Baier, J.A., McIlraith, S.A.: On domain-independent heuristics for planning with qualitative preferences. In: AAAI Spring Symposium: Logical Formalizations of Commonsense Reasoning, pp. 7–12 (2007)Google Scholar
  3. 3.
    Bienvenu, M., Fritz, C., McIlraith, S.A.: Planning with qualitative temporal preferences. In: Proceedings of the 10th International Conference on the Principles of Knowledge Representation and Reasoning (KR 2006), pp. 134–144 (2006)Google Scholar
  4. 4.
    Bordini, R.H., Hübner, J.F., Vieira, R.: Jason and the golden fleece of agent-oriented programming. In: Bordini, R.H., Dastani, M., Dix, J., El Fallah Seghrouchni, A. (eds.) Multi-Agent Programming. MSASSO, vol. 15, pp. 3–37. Springer, Boston, MA (2005). Scholar
  5. 5.
    Boutilier, C., Brafman, R.I., Domshlak, C., Hoos, H.H., Poole, D.: CP-nets: a tool for representing and reasoning with conditional ceteris paribus preference statements. J. Artif. Intell. Res. 21, 135–191 (2004)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Brafman, R., Domshlak, C.: Preference handling - an introductory tutorial. AI Mag. 30(1), 58 (2009)CrossRefGoogle Scholar
  7. 7.
    Bratman, M.E.: Intention, Plans, and Practical Reason, vol. 10. Harvard University Press, Cambridge (1987)Google Scholar
  8. 8.
    Cranefield, S., Winikoff, M., Dignum, V., Dignum, F.: No pizza for you: value-based plan selection in BDI agents. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI 2017), pp. 178–184 (2017)Google Scholar
  9. 9.
    Dastani, M.: 2APL: a practical agent programming language. Auton. Agent. Multi-Agent Syst. 16(3), 214–248 (2008)CrossRefGoogle Scholar
  10. 10.
    Deljoo, A., van Engers, T., Gommans, L., et al.: What is going on: utility-based plan selection in BDI agents. In: Proceedings of Workshops at the 31st AAAI Conference on Artificial Intelligence, pp. 711–718 (2017)Google Scholar
  11. 11.
    Erol, K., Hendler, J., Nau, D.S.: HTN planning: complexity and expressivity. In Proceedings of the 12th AAAI Conference on Artificial Intelligence, pp. 1123–1129 (1994)Google Scholar
  12. 12.
    Fisher, M., Bordini, R.H., Hirsch, B., Torroni, P.: Computational logics and agents: a road map of current technologies and future trends. Comput. Intell. 23(1), 61–91 (2007)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Gerevini, A., Long, D.: Plan constraints and preferences in PDDL3. Technical report (2005)Google Scholar
  14. 14.
    Gonzales, C., Perny, P.: GAI networks for utility elicitation. In: Proceedings of the 9th International Conference on the Principles of Knowledge Representation and Reasoning (KR 2004), pp. 224–233 (2004)Google Scholar
  15. 15.
    Hindriks, K.V.: Programming rational agents in GOAL. In: El Fallah Seghrouchni, A., Dix, J., Dastani, M., Bordini, R.H. (eds.) Multi-Agent Programming, pp. 119–157. Springer, Boston, MA (2009). Scholar
  16. 16.
    Hoeve, E.T., Dastani, M.: 3APL platform. Master’s thesis, University of Utrecht, The Netherlands (2003)Google Scholar
  17. 17.
    Jorge, A., McIlraith, S.A.: Planning with preferences. AI Mag. 29(4), 25–36 (2008)CrossRefGoogle Scholar
  18. 18.
    Marthi, B., Russell, S.J., Wolfe, J.: Angelic semantics for high-level actions. In: Proceedings of the 17th International Conference on Automated Planning and Scheduling, pp. 232–239 (2007)Google Scholar
  19. 19.
    McDermott, D., et al.: PDDL - the planning domain definition language. Technical report (1998)Google Scholar
  20. 20.
    Pigozzi, G., Tsoukiàs, A., Viappiani, P.: Preferences in artificial intelligence. Ann. Math. Artif. Intell. 77(3–4), 361–401 (2016)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Rao, A.S.: AgentSpeak(L): BDI agents speak out in a logical computable language. In: Van de Velde, W., Perram, J.W. (eds.) MAAMAW 1996. LNCS, vol. 1038, pp. 42–55. Springer, Heidelberg (1996). Scholar
  22. 22.
    Rao, A.S., Georgeff, M.P.: BDI agents: from theory to practice. In: Proceedings of the First International Conference on Multi-Agent Systems (ICMAS 1995), pp. 312–319 (1995)Google Scholar
  23. 23.
    Sileno, G.: Aligning law and action. Ph.D. thesis, University of Amsterdam (2016)Google Scholar
  24. 24.
    Sileno, G., Boer, A., van Engers, T.: A constructivist approach to rule bases. In: Proceedings of the 7th International Conference on Agents and Artificial Intelligence (ICAART 2015) (2015)Google Scholar
  25. 25.
    Visser, S., Thangarajah, J., Harland, J.: Reasoning about preferences in intelligent agent systems. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI 2011), pp. 426–431 (2011)Google Scholar
  26. 26.
    Visser, S., Thangarajah, J., Harland, J., Dignum, F.: Preference-based reasoning in BDI agent systems. Auton. Agent. Multi-Agent Syst. 30(2), 291–330 (2016)CrossRefGoogle Scholar
  27. 27.
    Yang, Q.: Formalizing planning knowledge for hierarchical planning. Comput. Intell. 6(1), 12–24 (1990)CrossRefGoogle Scholar
  28. 28.
    Yao, Y., Logan, B.: Action-level intention selection for BDI agents. In: Proceedings of the 15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2016), pp. 1227–1236 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Informatics InstituteUniversity of AmsterdamAmsterdamThe Netherlands
  2. 2.Leibniz Institute, University of Amsterdam/TNOAmsterdamThe Netherlands

Personalised recommendations