Advertisement

CaFé: A Group Process to Rationalize Technologies in Hybrid AAMAS Systems

  • H. Van Dyke Parunak
  • Marcus Huber
  • Randolph Jones
  • Michael Quist
  • Jack Zaientz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8758)

Abstract

Most agent research seeks insights about a single technology, and problems are chosen to allow this focus. In contrast, many real-world applications do not lend themselves to a single technology, but require multiple tools. In an applied AI company, each tool often has its own advocate, whose specialized knowledge may lead her to overestimate her tool’s contribution and diminish that of other tools. To form an effective team, the various members must have a shared understanding of how their tools complement one another. This paper describes CaFé (“Cases-Features”), a group process that we have prototyped for building a consensus mapping between tools and real-world problems. The five AI technologies encompassed in our prototype are cognitive architectures, intelligent user interfaces, classic multi-agent system paradigms, statistics and machine learning, and swarming. Structured group discussion identifies the dimensions of a feature space in which the technologies are distinct. The scheme that emerged from our exercise does not pretend to be an exhaustive characterization of the techniques, but it is a jointly owned map of our technology capabilities that has proven useful in design of new use cases.

Keywords

Feature Space Multiagent System Software Quality Data Visualizer Cognitive Architecture 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Anderson, J.R., Bothell, D., Byrne, M.D., Douglass, S., Lebiere, C., Qin, Y.: An integrated theory of the mind. Psychological Review 111(4), 1036–1060 (2004)CrossRefGoogle Scholar
  2. 2.
    Bourque, P., Fairley, R.E. (eds.): SWEBOK 3.0: Guide to the Software Engineering Body of Knowledge, 3rd edn. IEEE, Piscataway (2014)Google Scholar
  3. 3.
    CISQ: CISQ Specifications for Automated Quality Characteristic Measures. Object Management Group (2012), http://it-cisq.org/wp-content/uploads/2012/09/CISQ-Specification-for-Automated-Quality-Characteristic-Measures.pdf
  4. 4.
    de Penning, L., d’Avila Garcez, A.S., Lamb, L.C., Meyer, J.-J.C.: Neural-Symbolic Cognitive Agents: Architecture, Theory and Application. In: Lomuscio, A., Scerri, P. (eds.) The 13th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2014), pp. 1621–1622. IFAAMAS, Paris (2014)Google Scholar
  5. 5.
    Department of Defense: JP 3-09.3, Close Air Support. Washington, DC, Department of Defense (2009)Google Scholar
  6. 6.
    Department of the Army: FM 2-22.3 (FM 34-52), Human Intelligence Collector Operations. Washington, DC, Department of the Army (2006)Google Scholar
  7. 7.
    Ferguson, I.A.: Touring Machines: Autonomous Agents with Attitudes. Computer 25(5), 51–55 (1992)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Fischer, K., Muller, J.P., Pischel, M.: InteRRaP: Unifying Control in a Layered Agent Architecture. German Research Center for Artificial Intelligence, Saarbrucken (1995), http://www.dfki.uni-sb.de/~pischel/interrap.html
  9. 9.
    Hauser, R., Clausing, D.: The House of Quality. Harvard Business Review 66, 63–73 (1988)Google Scholar
  10. 10.
    Huber, M.J., Kumar, S., Lisse, S.A., McGee, D.: Integrating Authority, Deontics, and Deontics and Communications within a Joint Intention Framework. In: Huhns, M., Shehory, O. (eds.) The 2007 International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2007). IFAAMAS, Honolulu (2007)Google Scholar
  11. 11.
    Huber, M.J., Kumar, S., McGee, D.: Toward a Suite of Performatives based upon Joint Intention Theory. In: The AAMAS 2004 Workshop on Agent Communication (AC 2004), New York, NY (2004)Google Scholar
  12. 12.
    ISO: ISO/IEC 25010:2011: Systems and software engineering – Systems and software Quality Requirements and Evaluation (SQuaRE) – System and software quality models ISO (2011) Google Scholar
  13. 13.
    Laird, J.E.: The Soar Cognitive Architecture. MIT Press, Cambridge (2012)Google Scholar
  14. 14.
    Lesser, V., Corkill, D.: Challenges for Multi-Agent Coordination Theory Based on Empirical Observations. In: Lomuscio, A., Scerri, P. (eds.) The 13th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2014), pp. 1157–1160. IFAAMAS, Paris (2014)Google Scholar
  15. 15.
    Parunak, H.V.D.: ‘Go to the Ant’: Engineering Principles from Natural Agent Systems. Annals of Operations Research 75, 69–101 (1997)CrossRefzbMATHGoogle Scholar
  16. 16.
    Van Dyke Parunak, H., Nielsen, P., Brueckner, S., Alonso, R.: Hybrid Multi-agent Systems: Integrating Swarming and BDI Agents. In: Brueckner, S.A., Hassas, S., Jelasity, M., Yamins, D. (eds.) ESOA 2006. LNCS (LNAI), vol. 4335, pp. 1–14. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  17. 17.
    Quist, M., Yona, G.: A novel robust algorithm for structure-preserving embedding of metric and nonmetric spaces. Journal of Machine Learning Research 5, 399–430 (2004)MathSciNetzbMATHGoogle Scholar
  18. 18.
    Steinberg, A.N., Bowman, C.L.: Revisions to the JDL Data Fusion Model. In: Hall, D.L., Llinas, J. (eds.) Handbook of Multisensor Data Fusion, pp. 2.1–2.19. CRC Press, Boca Raton (2001)Google Scholar
  19. 19.
    Taylor, G., Quist, M., Hicken, A.: Acquiring Agent-based Models of Conflict from Event Data. In: IJCAI 2009. AAAI Press, Pasadena (2009)Google Scholar
  20. 20.
    Vesely, W., Stamatelatos, M., Dugan, J., Fragola, J., Minarick, J., Railsback III, J.: Fault Tree Handbook with Aerospace Applications. NASA, Washington, DC (2002), http://www.hq.nasa.gov/office/codeq/doctree/fthb.pdf Google Scholar
  21. 21.
    Wood, S.D., Zaientz, J.D., Beard, J., Fredriksen, R., Huber, M.: An Intelligent Interface-Agent Framework for Robotic Command and Control. In: The 2004 Command and Control Research and Technology Symposium, San Diego, CA (2004)Google Scholar
  22. 22.
    Wray, R.E., Jones, R.M.: An introduction to Soar as an agent architecture. In: Sun, R. (ed.) Cognition and Multi-agent Interaction: From Cognitive Modeling to Social Simulation, pp. 53–78. Cambridge University Press, Cambridge (2005)CrossRefGoogle Scholar
  23. 23.
    Zaientz, J.D., Beard, J.: Using Knowledge-Based Interface Design Techniques to Support Visual Analytics. In: Workshop on Intelligent User Interfaces for Intelligence Analysis at IUI 2006, Sydney, Australia (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • H. Van Dyke Parunak
    • 1
  • Marcus Huber
    • 1
  • Randolph Jones
    • 1
  • Michael Quist
    • 1
  • Jack Zaientz
    • 1
  1. 1.Soar Technology, Inc.Ann ArborUSA

Personalised recommendations