Exploring Heuristic Action Selection in Agent Programming

  • Koen V. Hindriks
  • Catholijn M. Jonker
  • Wouter Pasman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5442)


Rational agents programmed in agent programming lan- guages derive their choice of action from their beliefs and goals. One of the main benefits of such programming languages is that they facilitate a high-level and conceptually elegant specification of agent behaviour. Qualitative concepts alone, however, are not sufficient to specify that this behaviour is also nearly optimal, a quality typically also associated with rational agents. Optimality in this context refers to the costs and rewards associated with action execution. It thus would be useful to extend agent programming languages with primitives that allow the specification of near-optimal behaviour. The idea is that quantitative heuristics added to an agent program prune some of the options generated by the qualitative action selection mechanism. In this paper, we explore the expressivity needed to specify such behaviour in the Blocks World domain. The programming constructs that we introduce allow for a high-level specification of such heuristics due to the fact that these can be defined by (re)using the qualitative notions of the basic agent programming language again. We illustrate the use of these constructs by extending a Goal Blocks World agent with various strategies to optimize its behaviour.


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  1. 1.
    Slaney, J., Thiébaux, S.: Blocks World revisited. Artificial Intelligence 125, 119–153 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    de Boer, F., Hindriks, K., van der Hoek, W., Meyer, J.J.: A Verification Framework for Agent Programming with Declarative Goals. Journal of Applied Logic 5(2), 277–302 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Romero, A.G., Alquézar, R.: To block or not to block? In: Lemaître, C., Reyes, C.A., González, J.A. (eds.) IBERAMIA 2004. LNCS, vol. 3315, pp. 134–143. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  4. 4.
    Gupta, N., Nau, D.S.: On the Complexity of Blocks-World Planning. Artificial Intelligence 56(2-3), 223–254 (1992)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Bordini, R., Bazzan, A., Jannone, R., Basso, D., Vicari, R., Lesser, V.: AgentSpeak(XL): Efficient Intention Selection in BDI agents via Decision-Theoretic Task Scheduling. In: Proc. of the 1st Int. Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2002), pp. 1294–1302 (2002)Google Scholar
  6. 6.
    Thangarajah, J., Padgham, L., Winikoff, M.: Detecting and avoiding interference between goals in intelligent agents. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI 2003) (2003)Google Scholar
  7. 7.
    Boutilier, C., Reiter, R., Soutchanski, M., Thrun, S.: Decision-Theoretic, High-level Agent Programming in the Situation Calculus. In: Proc. of the 17th National Conference on Artificial Intelligence (AAAI 2000), pp. 355–362 (2000)Google Scholar
  8. 8.
    Ingrand, F., Georgeff, M., Rao, A.: An architecture for real-time reasoning and system control. IEEE Expert 7(6) (1992)Google Scholar
  9. 9.
    Rao, A.S.: AgentSpeak(L): BDI Agents Speak Out in a Logical Computable Language. In: Perram, J., Van de Velde, W. (eds.) MAAMAW 1996. LNCS, vol. 1038, pp. 42–55. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  10. 10.
    Cook, S., Liu, Y.: A Complete Axiomatization for Blocks World. Journal of Logic and Computation 13(4), 581–594 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Bacchus, F., Kabanza, F.: Using Temporal Logics to Express Search Control Knowledge for Planning. Artificial Intelligence 116(1-2), 123–191 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Cohen, P.R., Levesque, H.J.: Intention Is Choice with Commitment. Artificial Intelligence 42, 213–261 (1990)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Boutilier, C., Dean, T., Hanks, S.: Decision-theoretic planning: Structural assumptions and computational leverage. Journal of AI Research 11, 1–94 (1999)MathSciNetzbMATHGoogle Scholar
  14. 14.
    Hindriks, K.: Modules as policy-based intentions: Modular agent programming in goal. In: Dastani, M., El Fallah Seghrouchni, A., Ricci, A., Winikoff, M. (eds.) ProMAS 2007. LNCS, vol. 4908, pp. 156–171. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  15. 15.
  16. 16.
    de Giacomo, G., Levesque, H.J.: An incremental interpreter for high-level programs with sensing. Technical report, Department of Computer Science, University of Toronto (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Koen V. Hindriks
    • 1
  • Catholijn M. Jonker
    • 1
  • Wouter Pasman
    • 1
  1. 1.EEMCS, Delft University of TechnologyDelftThe Netherlands

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