Autonomous Agents and Multi-Agent Systems

, Volume 29, Issue 4, pp 569–620 | Cite as

A logic of intention and action for regular BDI agents based on bisimulation of agent programs

Article

Abstract

We address the problem of providing a computationally grounded semantics for belief, desire, intention (BDI) agents that explicitly relates intention to action, using as a basis for this connection a notion of bisimulation for agent programs. We first define regular BDI agents, a class of BDI agents inspired by the procedural reasoning system architecture, under the restriction that agent programs are representable as regular expressions. The operational semantics of regular agent programs is formalized using agent program execution graphs, an extension of the process graphs used to formalize regular processes. An agent’s executed program represents an attempt to perform an intended plan and can include branches for both successful execution and the failure of action attempts; intended execution paths are defined in terms of successful executions, and intentions in terms of future successfully executed agent programs. We present Agent Dynamic Logic (\(\mathsf {ADL}\)), a logic of intention and action that faithfully represents the operational semantics of regular BDI agents. \(\mathsf {ADL}\) is a logic in the spirit of BDI logic but also includes the dynamic logic of actions and a reduction of the logic of intention to the logics of action and time. A main contribution of the paper is a completeness result for a subclass of finite \(\mathsf {ADL}\) theories with explicit representations of agent plans.

Keywords

Rational agents Dynamic logic Temporal logic  Bisimulation 

Notes

Acknowledgments

This work was initially funded by an Australian Research Council Discovery Project Grant. Discussions with Krystian Ji have helped greatly in clarifying the main issues addressed in this paper. Thanks also to participants of the Otago Workshop on Logic and Multi-Agent Systems, the Decision Systems Laboratory at the University of Wollongong, the AAAI 2007 Stanford Spring Symposium on Intentions in Intelligent Systems, and two Australian Knowledge Representation Conventicles, for feedback on earlier versions of these ideas, and to the anonymous reviewers for comments on an earlier version of this paper.

References

  1. 1.
    Alechina, N., Dastani, M., & Logan, B. (2011). Reasoning about plan revision in BDI agent programs. Theoretical Computer Science, 412, 6115–6134.MATHMathSciNetCrossRefGoogle Scholar
  2. 2.
    Baldoni, M., Baroglio, C., Chopra, A. K., Desai, N., Patti, V., & Singh, M. P. (2009). Choice, interoperability, and conformance in interaction protocols and service choreographies. In Proceedings of the eighth international joint conference on autonomous agents and multiagent systems (pp. 843–850).Google Scholar
  3. 3.
    Belnap, N. D., & Perloff, M. (1988). Seeing to it that: A canonical form for agentives. Theoria, 54, 175–199.CrossRefGoogle Scholar
  4. 4.
    Bergstra, J. A., & Klop, J. W. (1984). The algebra of recursively defined processes and the algebra of regular processes. In J. Paredaens (Ed.), Automata, languages and programming. Berlin: Springer.Google Scholar
  5. 5.
    Bordini, R. H., Hübner, J. F., & Wooldridge, M. (2007). Programming multi-agent systems in AgentSpeak using Jason. Chichester: Wiley.MATHCrossRefGoogle Scholar
  6. 6.
    Bordini, R. H., & Moreira, Á. F. (2004). Proving BDI properties of agent-oriented programming languages: The asymmetry thesis principles in AgentSpeak(L). Annals of Mathematics and Artificial Intelligence, 42, 197–226.MATHMathSciNetCrossRefGoogle Scholar
  7. 7.
    Bratman, M. E. (1987). Intention, plans, and practical reason. Cambridge, MA: Harvard University Press.Google Scholar
  8. 8.
    Bratman, M. E. (1990). What is intention? In P. R. Cohen, J. Morgan, & M. E. Pollack (Eds.), Intentions in communication. Cambridge, MA: MIT Press.Google Scholar
  9. 9.
    Bratman, M. E., Israel, D. J., & Pollack, M. E. (1988). Plans and resource-bounded practical reasoning. Computational Intelligence, 4, 349–355.CrossRefGoogle Scholar
  10. 10.
    Broersen, J. (2011). Modeling attempt and action failure in probabilistic stit logic. In Proceedings of the twenty-second international joint conference on artificial intelligence (pp. 792–797).Google Scholar
  11. 11.
    Cohen, P. R., & Levesque, H. J. (1990). Intention is choice with commitment. Artificial Intelligence, 42, 213–261.MATHMathSciNetCrossRefGoogle Scholar
  12. 12.
    Corradini, F., De Nicola, R., & Labella, A. (2002). An equational axiomatization of bisimulation over regular expressions. Journal of Logic and Computation, 12, 301–320.MATHMathSciNetCrossRefGoogle Scholar
  13. 13.
    d’Inverno, M., Luck, M. M., Georgeff, M. P., Kinny, D. N., & Wooldridge, M. J. (2004). The dMARS architecture: A specification of the distributed multi-agent reasoning system. Autonomous Agents and Multi-Agent Systems, 9, 5–53.CrossRefGoogle Scholar
  14. 14.
    Emerson, E. A., & Clarke, E. M. (1982). Using branching time temporal logic to synthesize synchronization skeletons. Science of Computer Programming, 2, 241–266.MATHCrossRefGoogle Scholar
  15. 15.
    Emerson, E. A., & Halpern, J. Y. (1985). Decision procedures and expressiveness in the temporal logic of branching time. Journal of Computer and Systems Sciences, 30, 1–24.MATHMathSciNetCrossRefGoogle Scholar
  16. 16.
    Fagin, R., Halpern, J. Y., Moses, Y., & Vardi, M. Y. (1995). Reasoning about knowledge. Cambridge, MA: MIT Press.MATHGoogle Scholar
  17. 17.
    Fokkink, W. (1996). On the completeness of the equations for the Kleene star in bisimulation. In M. Wirsing & M. Nivat (Eds.), Algebraic methodology and software technology. Berlin: Springer.Google Scholar
  18. 18.
    Georgeff, M. P., & Ingrand, F. F. (1989). Decision-making in an embedded reasoning system. In Proceedings of the eleventh international joint conference on artificial intelligence (pp. 972–978).Google Scholar
  19. 19.
    Georgeff, M. P., & Lansky, A. L. (1987). Reactive reasoning and planning. In Proceedings of the sixth national conference on artificial intelligence (AAAI-87) (pp. 677–682).Google Scholar
  20. 20.
    Goldblatt, R. (1992). Logics of time and computation (2nd ed.). Stanford, CA: Center for the study of language and information.Google Scholar
  21. 21.
    Hindriks, K. V., de Boer, F. S., & van der Hoek, W. (1999). Agent programming in 3APL. Autonomous Agents and Multi-Agent Systems, 2, 357–401.CrossRefGoogle Scholar
  22. 22.
    Hindriks, K. V., & Meyer, J.-J. Ch. (2006). Agent logics as program logics: Grounding KARO. In C. Freksa, M. Kohlhase, & K. Schill (Eds.), KI 2006: Advances in artificial intelligence. Berlin: Springer.Google Scholar
  23. 23.
    Hindriks, K. V., & Meyer, J.-J. Ch. (2009). Toward a programming theory for rational agents. Autonomous Agents and Multi-Agent Systems, 19, 4–29.CrossRefGoogle Scholar
  24. 24.
    Howden, N., Rönnquist, R., Hodgson, A., & Lucas, A. (2001). JACK Intelligent Agents\(^{{\rm TM}}\): Summary of an agent infrastructure. Paper presented at the second international workshop on infrastructure for agents, MAS, and scalable MAS, Montreal, May 29, 2001.Google Scholar
  25. 25.
    Huber, M. J. (1999). JAM: A BDI-theoretic mobile agent architecture. In Proceedings of the third international conference on autonomous agents (pp. 236–243).Google Scholar
  26. 26.
    Ingrand, F. F., Chatila, R., Alami, R., & Robert, F. (1996). PRS: A high level supervision and control language for autonomous mobile robots. In Proceedings of the 1996 IEEE international conference on robotics and automation (pp. 43–49).Google Scholar
  27. 27.
    Lee, J., Huber, M. J., Kenny, P. G., & Durfee, E. H. (1994). UM-PRS: An implementation of the procedural reasoning system for multirobot applications. In Conference on intelligent robotics in field, factory, service, and space (pp. 842–849).Google Scholar
  28. 28.
    Lespérance, Y., Levesque, H. J., Lin, F., & Scherl, R. B. (2000). Ability and knowing how in the situation calculus. Studia Logica, 66, 165–186.MATHMathSciNetCrossRefGoogle Scholar
  29. 29.
    Levesque, H. J., Reiter, R., Lespérance, Y., Lin, F., & Scherl, R. B. (1997). GOLOG: A logic programming language for dynamic domains. Journal of Logic Programming, 31, 59–83.MATHMathSciNetCrossRefGoogle Scholar
  30. 30.
    Milner, R. (1984). A complete inference system for a class of regular behaviours. Journal of Computer and System Sciences, 28, 439–466.MATHMathSciNetCrossRefGoogle Scholar
  31. 31.
    Morley, D., & Myers, K. L. (2004). The SPARK agent framework. In Proceedings of the third international joint conference on autonomous agents and multiagent systems(pp. 714–721).Google Scholar
  32. 32.
    Myers, K. L. (1996). A procedural knowledge approach to task-level control. In Proceedings of the third international conference on artificial intelligence planning systems (pp. 158–165).Google Scholar
  33. 33.
    Nwana, H. S., Ndumu, D. T., Lee, L. C., & Collis, J. C. (1999). ZEUS: A toolkit for building distributed multiagent systems. Applied Artificial Intelligence, 13, 129–185.CrossRefGoogle Scholar
  34. 34.
    Padmanabhan, V., Governatori, G., & Sattar, A. (2001). Actions made explicit in BDI. In M. Stumptner, D. Corbett, & M. Brooks (Eds.), AI 2001: Advances in artificial intelligence. Berlin: Springer.Google Scholar
  35. 35.
    Plotkin, G. D. (1981). A structural approach to operational semantics. Technical Report DAIMI FN-19, Department of Computer Science, University of Aarhus.Google Scholar
  36. 36.
    Pokahr, A., Braubach, L., & Lamersdorf, W. (2005). A flexible BDI architecture supporting extensibility. In Proceedings of the 2005 IEEE/WIC/ACM international conference on intelligent agent technology (pp. 379–385).Google Scholar
  37. 37.
    Pollack, M. E., & Horty, J. F. (1999). There’s more to life than making plans: Plan management in dynamic, multiagent environments. AI Magazine, 20(4), 71–83.Google Scholar
  38. 38.
    Pratt, V. R. (1976). Semantical considerations on Floyd–Hoare logic. In Proceedings of the seventeenth IEEE symposium on foundations of computer science (pp. 109–121).Google Scholar
  39. 39.
    Rao, A. S. (1996). AgentSpeak(L): BDI agents speak out in a logical computable language. In W. Van de Velde & J. W. Perram (Eds.), Agents breaking away. Berlin: Springer.Google Scholar
  40. 40.
    Rao, A. S., & Georgeff, M. P. (1991). Asymmetry thesis and side-effect problems in linear-time and branching-time intention logics. In Proceedings of the twelfth international joint conference on artificial intelligence (pp. 498–504).Google Scholar
  41. 41.
    Rao, A. S., & Georgeff, M. P. (1991). Modeling rational agents within a BDI-architecture. In Proceedings of the second international conference on principles of knowledge representation and reasoning (pp. 473–484).Google Scholar
  42. 42.
    Rao, A. S., & Georgeff, M. P. (1992). An abstract architecture for rational agents. In Proceedings of the third international conference on principles of knowledge representation and reasoning (pp. 439–449).Google Scholar
  43. 43.
    Rao, A. S., & Georgeff, M. P. (1995). BDI agents: From theory to practice. In Proceedings of the first international conference on multi-agent systems (pp. 312–319).Google Scholar
  44. 44.
    Rao, A. S., & Georgeff, M. P. (1998). Decision procedures for BDI logics. Journal of Logic and Computation, 8, 293–343.MATHMathSciNetCrossRefGoogle Scholar
  45. 45.
    Rosenschein, S. J., & Kaelbling, L. P. (1986). The synthesis of digital machines with provable epistemic properties. In J. Y. Halpern (Ed.), Theoretical aspects of reasoning about knowledge: Proceedings of the 1986 conference. Los Altos, CA: Morgan Kaufmann.Google Scholar
  46. 46.
    Salomaa, A. (1966). Two complete axiom systems for the algebra of regular events. Journal of the Association for Computing Machinery, 13, 158–169.MATHMathSciNetCrossRefGoogle Scholar
  47. 47.
    Schild, K. (2000). On the relationship between BDI logics and standard logics of concurrency. Autonomous Agents and Multi-Agent Systems, 3, 259–283.CrossRefGoogle Scholar
  48. 48.
    Schmidt, R. A., Tishkovsky, D., & Hustadt, U. (2004). Interactions between knowledge, action and commitment within agent dynamic logic. Studia Logica, 78, 381–415.MATHMathSciNetCrossRefGoogle Scholar
  49. 49.
    Segerberg, K. (1989). Bringing it about. Journal of Philosophical Logic, 18, 327–347.MATHMathSciNetCrossRefGoogle Scholar
  50. 50.
    Singh, M. P. (1992). A critical examination of the Cohen–Levesque theory of intentions. In Proceedings of the tenth european conference on artificial intelligence (pp. 364–368).Google Scholar
  51. 51.
    Singh, M. P. (1998). Semantical considerations on intention dynamics for BDI agents. Journal of Experimental and Theoretical Artificial Intelligence, 10, 551–564.MATHCrossRefGoogle Scholar
  52. 52.
    Su, K., Sattar, A., Wang, K., Luo, X., Governatori, G., & Padmanabhan, V. (2005). Observation-based model for BDI-agents. In Proceedings of the twentieth national conference on artificial intelligence (AAAI-05) (pp. 190–195).Google Scholar
  53. 53.
    van Linder, B., & van der Hoek, W. (1998). Formalizing abilities and opportunities of agents. Fundamenta Informaticae, 34, 53–101.MATHMathSciNetGoogle Scholar
  54. 54.
    Wobcke, W. R. (2000). On the correctness of PRS agent programs. In N. R. Jennings & Y. Lespérance (Eds.), Intelligent agents VI. Berlin: Springer.Google Scholar
  55. 55.
    Wobcke, W. R. (2002). Modelling PRS-like agents’ mental states. In M. Ishizuka & A. Sattar (Eds.), PRICAI 2002: Trends in artificial intelligence. Berlin: Springer.Google Scholar
  56. 56.
    Wobcke, W. R. (2004). Model theory for PRS-like agents: Modelling belief update and action attempts. In C. Zhang, H. W. Guesgen, & W. K. Yeap (Eds.), PRICAI 2004: Trends in artificial intelligence. Berlin: Springer.Google Scholar
  57. 57.
    Wobcke, W. R. (2006). An analysis of three puzzles in the logic of intention. In A. Sattar & B.-H. Kang (Eds.), AI 2006: Advances in artificial intelligence. Berlin: Springer.Google Scholar
  58. 58.
    Wobcke, W. R., & Sichanie, A. G. (1999). A reactive scheduling agent architecture for coordinating autonomous assistants. In J. Liu & N. Zhong (Eds.), Intelligent agent technology: Systems, methodologies, and tools. Singapore: World Scientific.Google Scholar
  59. 59.
    Wooldridge, M. J. (2000). Computationally grounded theories of agency. In Proceedings of the fourth international conference on multi-agent systems (pp. 13–22).Google Scholar
  60. 60.
    Wooldridge, M. J. (2000). Reasoning about rational agents. Cambridge, MA: MIT Press.MATHGoogle Scholar

Copyright information

© The Author(s) 2014

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringUniversity of New South WalesSydneyAustralia

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