Skip to main content
Log in

Preference-based reasoning in BDI agent systems

  • Published:
Autonomous Agents and Multi-Agent Systems Aims and scope Submit manuscript

Abstract

An important feature of BDI agent systems is number of different ways in which an agent can achieve its goals. The choice of means to achieve the goal in made by the system at run time, depending on contextual information that is not available in advance. In this article, we explore ways that the user of an agent system can specify preferences which can be incorporated into the BDI execution process and used to guide the choices made. For example, a user of a travel system can specify a preferred airline, or a particular kind of accommodation, and the system will use this information to satisfy the goal and preferences, if possible. Preferences are specified in terms of properties of goals and resource usage, and are used to make two types of decisions: (a) select a plan when there is a choice and (b) determine the order in which subgoals of a plan should be pursued when their order is not fixed by design. We have implemented our preference framework in Jadex, and provide detailed case studies within the context of a holiday travel agent application.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. http://mmi.tudelft.nl/trac/goal.

  2. The value \(null\) serves a particular purpose that we have explained in Sect. 3.1.

  3. This predicate was included because the resource usage does not always need to be minimized when other preferences are taken into consideration.

  4. Conditions (2) and (3) are used for properties that were merged using \({\mathcal {N}}\) as described in Sect. 3.1.2. We provide a detailed example in Sect. 5.4

  5. The procedure to be used is determined by the designer prior to execution. Other reasonable choices for this procedure can be used if the designer so wishes.

References

  1. Baier, J.A., & McIlraith, S.A. (2007). On domain-independent heuristics for planning with qualitative preferences. In 7th Workshop on Nonmonotonic Reasoning, Action and Change (NRAC).

  2. Bienvenu, M., Fritz, C., & McIlraith, S.A. (2006). Planning with qualitative temporal preferences. In KR (pp. 134–144). AAAI Press.

  3. Boutilier, C., Reiter, R., Soutchanski, M., & Thrun, S. (2000). Decision-theoretic, high-level agent programming in the situation calculus. In AAAI (pp. 355–362). AAAI Press/The MIT Press.

  4. Busetta, P., Ronnquist, R., Hodgson, A., & Lucas, A. (1999). Jack intelligent agents—components for intelligent agents in java.

  5. Clement, B.J., & Durfee, E.H. (1999). Theory for coordinating concurrent hierarchical planning agents using summary information. In AAAI (pp. 495–502).

  6. Clement, B.J., & Durfee, E.H. (1999). Top-down search for coordinating the hierarchical plans of multiple agents. In Agents (pp. 252–259).

  7. Dasgupta, A., & Ghose, A. K. (2011). Bdi agents with objectives and preferences. In A. Omicini, S. Sardina, & W. Vasconcelos (Eds.), Declarative agent languages and technologies VIII. Lecture notes in computer science (Vol. 6619, pp. 22–39). Berlin: Springer.

    Chapter  Google Scholar 

  8. Fikes, R., & Nilsson, N. J. (1971). Strips: A new approach to the application of theorem proving to problem solving. Artificial Intelligence, 2(3/4), 189–208.

    Article  MATH  Google Scholar 

  9. Fox, M., & Long, D. (2003). Pddl2.1: An extension to pddl for expressing temporal planning domains. Journal of Artificial Intelligence Research (JAIR), 20, 61–124.

    MATH  Google Scholar 

  10. Fritz, C., & McIlraith, S. (2005). Compiling qualitative preferences into decision-theoretic golog programs. In Proceedings of The 6th Workshop on Nonmonotonic Reasoning, Action, and Change. Edinburgh, UK.

  11. Fritz, C., & McIlraith, S. (2006). Decision-theoretic golog with qualitative preferences. In Proceedings of KR’2006 (pp. 153–163). Lake District, UK.

  12. Gerevini, A., & Long, D. (2005). Plan constraints and preferences in pddl3—The language of the fifth international planning competition. Technical report.

  13. Ghallab, M., Isi, C.K., Penberthy, S., Smith, D.E., Sun, Y., & Weld, D. (1998). Pddl—The planning domain definition language. Technical report, CVC TR-98-003/DCS TR-1165, Yale Center for Computational Vision and Control.

  14. Hindriks, K. V., Jonker, C. M., & Pasman, W. (2008). Exploring heuristic action selection in agent programming. ProMAS. Lecture notes in computer science (Vol. 5442, pp. 24–39). Berlin: Springer.

    Google Scholar 

  15. Hindriks, K. V., & Birna van Riemsdijk, M. (2008). Using temporal logic to integrate goals and qualitative preferences into agent programming. DALT. Lecture notes in computer science (Vol. 5397, pp. 215–232). Berlin: Springer.

    Google Scholar 

  16. Ingrand, F. F., Georgeff, M. P., & Rao, A. S. (1992). An architecture for real-time reasoning and system control. IEEE Expert, 7(6), 34–44.

    Article  Google Scholar 

  17. Myers, K.L., & Morley, D.N. (2001). Human directability of agents. In K-CAP (pp. 108–115). ACM.

  18. Myers, K.L., & Morley, D.N. (2002). Resolving conflicts in agent guidance. In Proceedings of the AAAI-02 Workshop on Preferences in AI and CP: Symbolic Approaches.

  19. Nguyen, A., & Wobcke, W. (2006). An adaptive plan-based dialogue agent: Integrating learning into a bdi architecture. In AAMAS (pp. 786–788). ACM.

  20. Padgham, L., & Singh, D. (2013). Situational preferences for bdi plans. In M.L. Gini, O. Shehory, T. Ito, & C.M. Jonker (Eds) AAMAS (pp. 1013–1020). IFAAMAS.

  21. Pokahr, A., Braubach, L., & Lamersdorf, W. (2005). Jadex: A BDI reasoning engine. Multi-agent programming (Vol. 9, pp. 149–174). New York: Springer.

    Chapter  Google Scholar 

  22. Rao, A.S., & Georgeff, M.P. (1992). An abstract architecture for rational agents. In KR (pp. 439–449).

  23. Rnnquist, R. (2007). The goal oriented teams (gorite) framework. In M. Dastani, A. El Fallah-Seghrouchni, A. Ricci, & M. Winikoff (Eds.), PROMAS. Lecture notes in computer science (Vol. 4908, pp. 27–41). Berlin: Springer.

    Google Scholar 

  24. Shaw, P. H., & Bordini, R. H. (2007). Towards alternative approaches to reasoning about goals. In M. Baldoni, T. Cao Son, M. Birna van Riemsdijk, & M. Winikoff (Eds.), DALT. Lecture notes in computer science (Vol. 4897, pp. 104–121). Berlin: Springer.

    Google Scholar 

  25. Shaw, P. H., & Bordini, R. H. (2010). An alternative approach for reasoning about the goal-plan tree problem. In H. Coelho, R. Studer, & M. Wooldridge (Eds.), ECAI. Frontiers in artificial intelligence and applications (Vol. 215, pp. 1035–1036). Amsterdam: IOS Press.

    Google Scholar 

  26. Shaw, P.H., Farwer, B., & Bordini, R.H. (2008). Theoretical and experimental results on the goal-plan tree problem. In L. Padgham, D.C. Parkes, J.P. Müller, & S. Parsons (Eds), AAMAS (3) (pp. 1379–1382). IFAAMAS.

  27. Thangarajah, J., Padgham, L., & Winikoff, M. (2003). Detecting & exploiting positive goal interaction in intelligent agents. In AAMAS (pp. 401–408). ACM.

  28. Thangarajah, J., Sardiña, S., & Padgham, L. (2012). Measuring plan coverage and overlap for agent reasoning. In W. van der Hoek, L. Padgham, V. Conitzer, & M. Winikoff (Eds), AAMAS (pp. 1049–1056). IFAAMAS.

  29. Thangarajah, J., Winikoff, M., Padgham, L., & Fischer, K. (2002). Avoiding resource conflicts in intelligent agents. In ECAI (pp. 18–22). IOS Press.

  30. Toranzo, C., Errecalde, M., & Ferretti, E. (2014). A framework for multi-criteria argumentation-based decision making within a bdi agent. Journal of Computer Science and Technology, 14(1), 46–54.

    Google Scholar 

  31. Winikoff, M., & Cranefield, S. On the testability of bdi agent systems. Discussion Paper 2008/03, Department of Information Science, University of Otago, http://eprints.otago.ac.nz/793/. Accessed 15 Jan 2015.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to John Thangarajah.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Visser, S., Thangarajah, J., Harland, J. et al. Preference-based reasoning in BDI agent systems. Auton Agent Multi-Agent Syst 30, 291–330 (2016). https://doi.org/10.1007/s10458-015-9288-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10458-015-9288-2

Keywords

Navigation