Automated Adaptation of Strategic Guidance in Multiagent Coordination

  • Rajiv T. Maheswaran
  • Pedro Szekely
  • Romeo Sanchez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7047)


We address multi-agent planning problems in dynamic environments motivated by assisting human teams in disaster emergency response. It is challenging because most goals are revealed during execution, where uncertainty in the duration and outcome of actions plays a significant role, and where unexpected events can cause large disruptions to existing plans. The key to our approach is giving human planners a rich strategy language to constrain the assignment of agents to goals and allow the system to instantiate the strategy during execution, tuning the assignment to the evolving execution state. Our approach outperformed an extensively-trained team coordinating with radios and a traditional command-center organization, and an agent-assisted team using a different approach.


Multi-Agent Systems Real-Time Coordination Human-Agent Collaboration Mixed-Initiative Approaches Disaster / Emergency Response 


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  1. 1.
    Ai-Chang, M., Bresina, J., Charest, L., Chase, A., Hsu, J.C.-j., Jonsson, A., Kanefsky, B., Morris, P., Rajan, K., Yglesias, J., Chafin, B.G., Dias, W.C., Maldague, P.F.: Mapgen: Mixed-initiative planning and scheduling for the mars exploration rover mission. IEEE Intelligent Systems 19(1), 8–12 (2004)CrossRefGoogle Scholar
  2. 2.
    Bacchus, F., Kabanza, F., De Sherbrooke, U.: Using temporal logics to express search control knowledge for planning. Artificial Intelligence 116, 2000 (1999)MathSciNetGoogle Scholar
  3. 3.
    Boddy, M., Horling, B., Phelps, J., Goldman, R.P., Vincent, R., Long, A.C., Kohout, B., Maheswaran, R.: CTAEMS language specification: Version 2.04 (2007)Google Scholar
  4. 4.
    Bresina, J., Meuleauy, N., Ramakrishnan, S., Smith, D., Washingtonx, R.: Planning under continuous time and resource uncertainty: A challenge for ai. In: Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence, pp. 77–84. Morgan Kaufmann (2002)Google Scholar
  5. 5.
    Burstein, M.H., McDermott, D.V.: Issues in the development of human-computer mixed-initiative planning systems. In: Cognitive Technology (1996)Google Scholar
  6. 6.
    Chen, Y., Wah, B., Hsu, C.W.: Temporal planning using subgoal partitioning and resolution in sgplan. Journal of Artificial Intelligence Research (JAIR) 26(1), 323–369 (2006)zbMATHGoogle Scholar
  7. 7.
    Fox, M., Long, D.: Modelling mixed discrete-continuous domains for planning. Journal of Artificial Intelligence Research (JAIR) 27 (2006)Google Scholar
  8. 8.
    Gerevini, A., Saetti, A., Serina, I., Toninelli, P.: Fast planning in domains with derived predicates: an approach based on rule-action graphs and local search. In: Proceedings of the 20th National Conference on Artificial Intelligence (AAAI), pp. 1157–1162. AAAI Press (2005)Google Scholar
  9. 9.
    Hayes, C.C., Larson, A.D., Ravinder, U.: Weasel: A mipas system to assist in military planning. In: ICAPS 2005 MIPAS Workshop, WS3 (2005)Google Scholar
  10. 10.
    Jin, J., Sanchez, R., Maheswaran, R.T., Szekely, P.A.: Vizscript: on the creation of efficient visualizations for understanding complex multi-agent systems. In: Intelligent User Interfaces, pp. 40–49 (2008)Google Scholar
  11. 11.
    Kleiner, A., Freiburg, U.: Wearable computing meets multiagent systems: A real-world interface for the robocuprescue simulation platform. In: First International Workshop on Agent Technology for Disaster Management at AAMAS 2006 (2006)Google Scholar
  12. 12.
    Myers, K.L., Jarvis, P.A., Mabry, W., Michael, T., Wolverton, J.: A mixed-initiative framework for robust plan sketching. In: Proceedings of the 13th International Conference on Automated Planning and Scheduling (2003)Google Scholar
  13. 13.
    Nau, D., Ilghami, O., Kuter, U., Murdock, J.W., Wu, D., Yaman, F.: Shop2: An htn planning system. Journal of Artificial Intelligence Research 20, 379–404 (2003)zbMATHGoogle Scholar
  14. 14.
    Nourbakhsh, I.R., Sycara, K., Koes, M., Yong, M., Lewis, M., Burion, S.: Human-robot teaming for search and rescue. IEEE Pervasive Computing 4(1), 72–78 (2005)CrossRefGoogle Scholar
  15. 15.
    Schurr, N., Marecki, J., Scerri, P., Lewis, J., Tambe, M.: The DEFACTO System: Coordinating human-agent teams for the future of disaster response. In: Programming Multi-Agent Systems: Third International Workshop. Springer, Heidelberg (2005)Google Scholar
  16. 16.
    Tate, A., Drabble, B., Dalton, J.: O-plan: A knowledge-based planner and its application to logistics. In: Advanced Planning Technology, pp. 259–266. AAAI Press (1996)Google Scholar
  17. 17.
    Tate, A.: Multi-agent planning via mutually constraining the space of behaviour. Tech. rep. In: Constraints and Agents: Papers from the 1997 AAAI Workshop (1997)Google Scholar
  18. 18.
    Veloso, M.M., Mulvehill, A.M., Cox, M.T.: Rationale-supported mixed-initiative case-based planning. In: Proceedings of the Ninth Annual Conference on Innovative Applications of Artificial Intelligence. Menlo., pp. 1072–1077. AAAI Press (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Rajiv T. Maheswaran
    • 1
  • Pedro Szekely
    • 1
  • Romeo Sanchez
    • 2
  1. 1.Information Sciences Institute and Department of Computer ScienceUniversity of Southern CaliforniaUSA
  2. 2.Facultad de Ingeniería Mecánica y EléctricaUniversidad Autónoma de Nuevo LeónMexico

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