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A capabilities-based model for adaptive organizations

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Abstract

Multiagent systems have become popular over the last few years for building complex, adaptive systems in a distributed, heterogeneous setting. Multiagent systems tend to be more robust and, in many cases, more efficient than single monolithic applications. However, unpredictable application environments make multiagent systems susceptible to individual failures that can significantly reduce its ability to accomplish its overall goal. The problem is that multiagent systems are typically designed to work within a limited set of configurations. Even when the system possesses the resources and computational power to accomplish its goal, it may be constrained by its own structure and knowledge of its member’s capabilities. To overcome these problems, we are developing a framework that allows the system to design its own organization at runtime. This paper presents a key component of that framework, a metamodel for multiagent organizations named the Organization Model for Adaptive Computational Systems. This model defines the requisite knowledge of a system’s organizational structure and capabilities that will allow it to reorganize at runtime and enable it to achieve its goals effectively in the face of a changing environment and its agent’s capabilities.

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References

  1. Bernon C., Cossentino M., Gleizes M., Turci P., Zambonelli F. (2005). A study of some multi-agent meta-models. In: Odell J., Giorgini P., Müller J. (eds). Agent-oriented software Engineering V: 5th Intl. Workshop (AOSE 2004), LNCS 3382. Berlin, Springer

    Google Scholar 

  2. Beydoun G., Low G., Gonzalez-Perez C., Henderson-Sellers B. (2006). Synthesis of a Generic MAS Metamodel. In: Garcia A., et. al (eds). Software Engineering for multi-agent systems IV: Research issues and practical applications Series, LNCS 3914. Berlin, Springer

    Google Scholar 

  3. Blau P.M., Scott W.R. (1962). Formal organizations. San Francisco, Chandler

    Google Scholar 

  4. Brinkkemper S. (1996). Method engineering: Engineering of information systems development methods and tools. Journal of Information and Software Technology, 38(4): 275–280

    Article  Google Scholar 

  5. Carley K.M. (1995). Computational and mathematical organization theory: Perspective and directions. Computational and Mathematical Organization Theory, 1(1): 39–56

    Article  Google Scholar 

  6. Carley K.M. (1998). Organizational adaptation. Annals of Operations Research, 75, 25–47

    Article  Google Scholar 

  7. Carley K.M., Gasser L. (1999). Computational organization theory. In: Weiss G. (eds). Multiagent systems: A modern approach to distributed artificial intelligence. Cambridge, MIT Press

    Google Scholar 

  8. Cohen P.R., Levesque H.J. (1990). Intention is choice with commitment. Artificial Intelligence, 42(3): 213–261

    Article  MathSciNet  MATH  Google Scholar 

  9. Cohen P.R., Levesque H.J. (1991). Teamwork. Nous, 25(4): 487–512

    Google Scholar 

  10. Coutinho, L., Sichman, J., & Boissier, O. (2005). Modeling organization in MAS: A comparison of models. In Proceedings of the 1st. Workshop on Software Engineering for Agent-Oriented Systems (SEAS’05). Uberlândia, Brazil, October 3, 2005.

  11. DeLoach, S. A. (2001). Analysis and design using MaSE and agentTool. In Proceedings of the 12th Midwest Artificial Intelligence and Cognitive Science Conference (MAICS 2001). Oxford, Ohio, March, 2001.

  12. DeLoach, S. A. (2006). Multiagent systems engineering of organization-based multiagent systems. In A. Garcia, et al. (Eds.), Software Engineering for multi-agent systems IV: Research issues and practical applications Series, Berlin: LNCS 3914, Springer.

  13. DeLoach, S. A., & Oyenan, W. H. (2006). An organizational model and dynamic goal model for autonomous, adaptive systems. Multiagent & Cooperative Robotics Laboratory Technical Report No. MACR-TR-2006-01. Kansas State University. March 13, 2006.

  14. DeLoach S.A., Wood M.F., Sparkman C.H. (2001). Multiagent systems engineering. The International Journal of Software Engineering and Knowledge Engineering, 11(3): 231–258

    Article  Google Scholar 

  15. Dignum, V. (2004). A model for organizational interaction: Based on agents, founded in logic. Ph.D. thesis, Utrecht University.

  16. Dignum, V., V’azquez-Salceda, J., & Dignum, F. (2004). Omni: Introducing social structure, norms and ontologies into agent organizations. In Programming Multi-Agent Systems: Second International Workshop (ProMAS 2004) LNCS 3346 (pp. 181–198). Berlin: Springer.

  17. Ferber, J., & Gutknecht, O. (1998). A meta-model for the analysis and design of organizations in multi-agent systems. In Proceedings of 3rd International Conference on MultiAgent Systems (ICMAS’98). (pp. 128–135).

  18. Ferber J., Gutknecht O., Michel F. (2003). From agents to organizations: an organizational view of multi-agent systems. In: Giorgini P., Muller J.P., Odell J. (eds). Agent-Oriented Software Engineering IV: 4th International Workshop (AOSE 2003), LNCS 2935. Berlin, Springer, pp. 214–230

    Google Scholar 

  19. Grosz B.J., Kraus S. (1996). Collaborative plans for complex group action. Artificial Intelligence, 86(2): 269–357

    Article  MathSciNet  Google Scholar 

  20. Henderson-Sellers B. (2005). Evaluating the feasibility of method engineering for the creation of agent-oriented methodologies. In: Pechoucek M., Petta P., Varga L. (eds). Multi-Agent Systems and Applications IV: 4th International Central and Eastern European Conference on Multi-agent Systems, LNCS 3690. Berlin, Springer, pp. 142–152

    Google Scholar 

  21. Horling, B., & Lesser, V. (2005). Using ODML to model multi-agent organizations. In Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology (pp. 72–80). 2005.

  22. Hübner, J., Sichman, J., & Boissier, O. (2002). MOISE+: Towards a structural, functional and deontic model for MAS Organization. In Proceedings of the 1st International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS’02) (pp. 501–502).

  23. Jennings N.R. (1992). Towards a cooperation knowledge level for collaborative problem solving. In: Neumann B. (eds). Proceedings of the 10th European Conference on Artificial Intelligence, (pp. 224–228). Vienna, Austria, 1992.

  24. Jennings N.R. (1993). Commitments and conventions: The foundation of coordination in multiagent systems. Knowledge Engineering Review, 8(3): 223–250

    Article  Google Scholar 

  25. Jennings N.R. (1995). Controlling cooperative problem solving in industrial multi-agent systems using joint intentions. Artificial Intelligence, 75(2): 195–240

    Article  Google Scholar 

  26. Juan, T., & Sterling, L. (2004). The ROADMAP meta-model for intelligent adaptive multi-agent systems in open environments. LNCS 2935 (pp. 53–68). Berlin: Springer.

  27. Kashyap, S. (2006). Reorganization in multiagent systems. MS Thesis, Kansas State University.

  28. Kinny D., Ljungberg M., Rao A.S., Sonenberg E., Tidhar G., Werner E. (1992). Planned team activity. In: Castelfranchi C., Werner E. (eds). Artificial social systems—selected papers from the fourth European workshop on Modeling Autonomous Agents in a Multi-Agent World (MAAMAW-92), LNAI 830. Berlin, Springer, pp. 226–256

    Google Scholar 

  29. Matson, E., & DeLoach, S. A. (2003). An organization-based adaptive information system for battlefield situational analysis. Proc. of the Intl Conf. on Integration of Knowledge Intensive Multi-Agent Systems: KIMAS’03: Modeling, Exploration, and Engineering, Boston, MA, Sep 30–Oct 3.

  30. Matson, E., & DeLoach, S. A. (2004). Integrating robotic sensor and effector capabilities with multi-agent organizations. Proc. of the Intl. Conf. on Artificial Intelligence (IC-AI’04), Las Vegas, NV.

  31. MESSAGE: Methodology for Engineering Systems of Software Agents, Deliverable 1. Initial Methodology. (2000). EURESCOM Project P907-GI, July 2000.

  32. Nair, R., Tambe, M., & Marsella, S. (2002). Team formation for reformation. In Proceedings of the AAAI Spring Symposium on Intelligent Distributed and Embedded Systems.

  33. Odell J., Nodine M., Levy R. (2005). A metamodel for agents, roles, and groups. In: Odell J., Giorgini P., Müller J. (eds). Agent-oriented software engineering V: 5th Intl. Workshop (AOSE 2004) LNCS 3382. Springer, Berlin

    Google Scholar 

  34. Peng, Z., & Heng, H. (2005). An improved agent/group/role meta-model for building multi-agent systems. In Proceedings of 2005 Intl. Conf. on Machine Learning and Cybernetics (pp. 287–292).

  35. Robby, DeLoach, S. A., & Kolesnikov, V. A. (2006). Using design metrics for predicting system flexibility. In L. Baresi & R. Heckel (Eds.), Proceedings of the 9th International Conference on Fundamental Approaches to Software Engineering (FASE 2006) LNCS 3922 (pp. 184–198).

  36. Robby, Dwyer, M. B., & Hatcliff, J. (2003). Bogor: An extensible and highly-modular model checking framework. In Proceedings of the 4th Joint Meeting of the European Software Engineering Conf. and ACM SIGSOFT Symp. on the Foundations of SW Engineering (ESEC/FSE 2003) (pp. 267–276).

  37. Russell, S., & Norvig, P. (2003). Artificial Intelligence a Modern Approach, Pearson Education.

  38. Sycara K. (1998). Multiagent systems. AI Magazine, 19(2): 79–92

    Google Scholar 

  39. Tambe M. (1997). Towards flexible teamwork. Journal of AI Research, 7, 83–124

    Google Scholar 

  40. Turner R.M., Turner E.H. (2001). A two-level, protocol-based approach to controlling autonomous oceanographic sampling networks. IEEE Journal of Oceanic Engineering, 26(4): 654–666

    Article  Google Scholar 

  41. van Lamsweerde A., Darimont R., Letier E. (1998). Managing conflicts in goal-driven requirements engineering. IEEE Transactions on Software Engineering, 24(11): 908–926

    Article  Google Scholar 

  42. Vazquez-Salceda J., Dignum F. (2003). Modelling electronic organizations. In: Marik V., Muller J., Pechoucek M. (eds). Multi-agent systems and applications III LNAI 2691. Berlin, Springer, pp. 584–593

    Chapter  Google Scholar 

  43. Wagner G. (2001). Agent-oriented analysis and design of organisational information systems. In: Barzdins J., Caplinskas A. (eds). Databases and information systems. Kluwer Academic Publishers, Norwell, MA, pp. 111–124

    Google Scholar 

  44. Wooldridge M., Jennings N.R., Kinny D. (2000). The gaia methodology for agent-oriented analysis and design. Journal of Autonomous Agents and Multi-Agent Systems, 3(3): 285-312

    Article  Google Scholar 

  45. Zambonelli F., Jennings N.R., Wooldridge M. (2001). Organisational abstractions for the analysis and design of multi-agent systems. In: Ciancarini P., Wooldridge M. (eds). Agent-oriented software engineering-Proc. of the First Intl. Workshop on Agent-Oriented Software Engineering, LNCS 1957. Berlin, Springer, pp. 207–222

    Google Scholar 

  46. Zambonelli F., Jennings N.R., Wooldridge M.J. (2001). Organisational rules as an abstraction for the analysis and design of multi-agent systems. International Journal of Software Engineering and Knowledge Engineering, 11(3): 303–328

    Article  Google Scholar 

  47. Zambonelli F., Jennings N.R., Omicini A., Wooldridge, M.J. (2001). Agent-oriented software engineering for internet applications. In: Omicini A., Zambonelli F., Klusch M., Tolksdorf R. (eds). Coordination of internet agents: Models, technologies, and applications. Berlin, Springer-Verlag, pp. 326–346

    Google Scholar 

  48. Zhong, C. (2006). An Investigation of Reorganization Algorithms. MS Thesis, Kansas State University.

  49. Zhong, C., & DeLoach, S. A. (2006). An investigation of reorganization algorithms. In Proceedings of the International Conference on Artificial Intelligence (IC-AI’2006), CSREA Press.

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Authors and Affiliations

  1. Department of Computing & Information Sciences, Kansas State University, 234 Nichols Hall, Manhattan, KS, 66506, USA

    Scott A. DeLoach & Walamitien H. Oyenan

  2. Department of Computer Science and Engineering, Wright State University, Dayton, OH, USA

    Eric T. Matson

Authors
  1. Scott A. DeLoach
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  2. Walamitien H. Oyenan
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  3. Eric T. Matson
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Correspondence to Scott A. DeLoach.

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DeLoach, S.A., Oyenan, W.H. & Matson, E.T. A capabilities-based model for adaptive organizations. Auton Agent Multi-Agent Syst 16, 13–56 (2008). https://doi.org/10.1007/s10458-007-9019-4

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