SDML: A Multi-Agent Language for Organizational Modelling

  • Scott Moss
  • Helen Gaylard
  • Steve Wallis
  • Bruce Edmonds
Article

Abstract

A programming language which is optimized for modelling multi-agent interaction within articulated social structures such as organizations is described with several examples of its functionality. The language is SDML, a strictly declarative modelling language which has object-oriented features and corresponds to a fragment of strongly grounded autoepistemic logic. The virtues of SDML include the ease of building complex models and the facility for representing agents flexibly as models of cognition as well as modularity and code reusability. Two representations of cognitive agents within organizational structures are reported and a Soar-to-SDML compiler is described. One of the agent representations is a declarative implementation of a Soar agent taken from the Radar-Soar model of Ye and Carley (1995). The Ye-Carley results are replicated but the declarative SDML implementation is shown to be much less computationally expensive than the more procedural Soar implementation. As a result, it appears that SDML supports more elaborate representations of agent cognition together with more detailed articulation of organizational structure than we have seen in computational organization theory. Moreover, by representing Soar-cognitive agents declaratively within SDML, that implementation of the Ye-Carley specification is necessarily consistent and sound with respect to the formal logic to which SDML corresponds.

simulation organization computational model formal logic cognition 

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References

  1. Barnard, P.J. (1985), “Interacting Cognitive Subsystems: A Psycholinguistic Approach to Short-Term Memory,” in A. Ellis (Ed.) Progress in the Psychology of Language, Hillsdale, NJ: Lawrence Erlbaum, Chapt. 6, pp. 197–258.Google Scholar
  2. Carley, K.M. and D.M. Svoboda (1996), “Modeling Organizational Adaptation as a Simulated Annealing Process,” Sociological Methods and Research, 25(1), 138–168.Google Scholar
  3. Carley, K.M. and Zhiang Lin (forthcoming), “A Theoretical Study of Organizational Performance under Information Distortion,” Management Science.Google Scholar
  4. Cohen, P.R. (1985), Heuristic Reasoning: An Artificial Intelligence Approach, Boston: Pitman Advanced Publishing Program.Google Scholar
  5. Cooper, R., J. Fox, J. Farringdon and T. Shallice (1996), “A Systematic Methodology for Cognitive Modeling,” Artificial Intelligence, 85, 3–44.Google Scholar
  6. Edmonds, B.M. (1997), “Modelling Socially Intelligent Agents in Organisations,” AAAI Fall Symposium on Socially Intelligent Agents and CPM Report No. 97-26 (http://www.cpm.mmu.ac.uk/cpmrep26.html).Google Scholar
  7. Gaylard, H.L. (1997), “A Formal Re-analysis of the Effects of Task Decomposition Scheme and Organizational Structure on Organizational Performance and Robustness,” CPM Report No. 97-29 (http://www.cpm.mmu.ac.uk/cpmrep29.html).Google Scholar
  8. Hunt, E. and R. Luce (1992), “Soar as a World-View, Not a Theory,” Behavioral and Brain Sciences, 15(3), 447–448.Google Scholar
  9. Johnson-Laird, P.N. (1983), Mental Models, Cambridge, UK: Cambridge University Press.Google Scholar
  10. Moss, S. (1995), “Control Metaphors in the Modelling of Decision-Making Behaviour,” Computational Economics, 8(4), 283–301.Google Scholar
  11. Moss, S., B. Edmonds and H. Gaylard, “Modelling R&D Strategy as a Network Search Problem,” The Multiple Linkages Between Technological Change and the Economy, Rome: CEIS.Google Scholar
  12. Newell, A. (1990), Unified Theories of Cognition, Cambridge, MA: Harvard University Press.Google Scholar
  13. So, Y. and E.H. Durfee (1996), “Designing Tree Structured Organizations for Computational Agents,” Computational and Mathematical Organization Theory, 2(3), Fall 1996.Google Scholar
  14. Tambe, M. and P.S. Rosenbloom (1996), “Architectures for Agents that Track Other Agents in Multi-Agent Worlds,” Intelligent Agents, II, Springer Verlag Lecture Notes in Artificial Intelligence (LNAI 1037).Google Scholar
  15. Verhagen, H. and M. Masuch (1994), “TASCCS: A Synthesis of Double-AISS and Plural-SOAR,” in K.M. Carley and M.J. Prietula (Eds.) Computational Organization Theory, Hillsdale, NJ: Lawrence ErlbaumGoogle Scholar
  16. Ye, M. and K.M. Carley (1995), “Radar-Soar: Towards an Artificial Organization Composed of Intelligent Agents,” Journal of Mathematical Sociology, 20(2–3), 219–246.Google Scholar

Copyright information

© Kluwer Academic Publishers 1998

Authors and Affiliations

  • Scott Moss
    • 1
  • Helen Gaylard
    • 1
  • Steve Wallis
    • 1
  • Bruce Edmonds
    • 1
  1. 1.Centre for Policy ModellingManchester Metropolitan UniversityManchesterU.K.

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