Efficient Auction Based Coordination for Distributed Multi-agent Planning in Temporal Domains Using Resource Abstraction

  • Andreas HertleEmail author
  • Bernhard Nebel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11117)


Recent advances in mobile robotics and AI promise to revolutionize industrial production. As autonomous robots are able to solve more complex tasks, the difficulty of integrating various robot skills and coordinating groups of robots increases dramatically. Domain independent planning promises a possible solution. For single robot systems a number of successful demonstrations can be found in scientific literature. However our experiences at the RoboCup Logistics League in 2017 highlighted a severe lack in plan quality when coordinating multiple robots. In this work we demonstrate how out of the box temporal planning systems can be employed to increase plan quality for temporal multi-robot tasks. An abstract plan is generated first and sub-tasks in the plan are auctioned off to robots, which in turn employ planning to solve these tasks and compute bids. We evaluate our approach on two planning domains and find significant improvements in solution coverage and plan quality.


  1. 1.
    Coles, A.J., Coles, A.I., Fox, M., Long, D.: Forward-chaining partial-order planning. In: Proceedings of the Twentieth International Conference on Automated Planning and Scheduling (ICAPS 2010), May 2010Google Scholar
  2. 2.
    Eyerich, P., Mattmüller, R., Röger, G.: Using the context-enhanced additive heuristic for temporal and numeric planning. In: Proceedings of the 19th International Conference on Automated Planning and Scheduling, ICAPS 2009, Thessaloniki, Greece, 19–23 September 2009 (2009)Google Scholar
  3. 3.
    Helmert, M.: The fast downward planning system. J. Artif. Intell. Res. 26, 191–246 (2006)CrossRefGoogle Scholar
  4. 4.
    Howey, R., Long, D., Fox, M.: Validating plans with exogenous events. In: Proceedings of the 23rd Workshop of the UK Planning and Scheduling Special Interest Group (2004)Google Scholar
  5. 5.
    Koehler, J., Ottiger, D.: An AI-based approach to destination control in elevators. AI Mag. 23(3), 59–78 (2002)Google Scholar
  6. 6.
    Niemueller, T., Karpas, E., Vaquero, T., Timmons, E.: Planning competition for logistics robots in simulation. In: WS on Planning and Robotics (PlanRob) at International Conference on Automated Planning and Scheduling (ICAPS) (2016)Google Scholar
  7. 7.
    Sacerdoti, E.D.: Planning in a hierarchy of abstraction spaces. Artif. Intell. 5(2), 115–135 (1974)CrossRefGoogle Scholar
  8. 8.
    Schpers, B., Niemueller, T., Lakemeyer, G., Gebser, M., Schaub, T.: ASP-based time-bounded planning for logistics robots. In: Proceedings of the Twenty-Eighth International Conference on Automated Planning and Scheduling (ICAPS 2018) (2018)Google Scholar
  9. 9.
    Smith, R.G.: The contract net protocol: high-level communication and control in a distributed problem solver. IEEE Trans. Comput. 29(12), 1104–1113 (1980)CrossRefGoogle Scholar
  10. 10.
    Srivastava, B., Kambhampati, S., Do, M.B.: Planning the project management way: efficient planning by effective integration of causal and resource reasoning in realplan. Artif. Intell. 131(1–2), 73–134 (2001)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of FreiburgFreiburgGermany

Personalised recommendations