Reasoning for Autonomous Agents in Dynamic Domains: Towards Automatic Satisfaction of the Module Property

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10839)


State-of-the-art service robots that fetch a cup of coffee and clean up rooms require cognitive skills such as learning, planning, and reasoning. Especially reasoning in dynamic and human populated environments demands for novel approaches that can handle comprehensive and fluent knowledge bases. Our long-term objective is an autonomous robotic team that is capable of handling dynamic and domestic environments. Therefore, we combined ALICA – A Language for Interactive Cooperative Agents – with the Answer Set Programming solver Clingo. The answer set programming approach offers multi-shot solving techniques and non-monotonic stable model semantics, but requires to keep the Module Property satisfied. We developed an automatic satisfaction of the Module Property and chose topological path planning as our evaluation scenario. We utilised the Region Connection Calculus as the underlying formalism of our evaluation and investigated the scalability of our implementation. The results show that our approach handles dynamic environments and scales up to appropriately large problem sizes while automatically satisfying the Module Property.


Answer Set Programming Region Connection Calculus Module Property Multi-shot solving 


  1. 1.
    Wurman, P.R., D’Andrea, R., Mountz, M.: Coordinating hundreds of cooperative, autonomous vehicles in warehouses. AI Mag. 29, 9–19 (2008)CrossRefGoogle Scholar
  2. 2.
    Hars, A.: Driverless Car Market Forecasts (2017). 9 May 2017
  3. 3.
    Tomizawa, F.: Who is NAO? (2017). 9 May 2017
  4. 4.
    Sullivan, A., Elkin, M., Umaschi Bers, M.: KIBO robot demo: engaging young children in programming and engineering. In: Proceedings of the 14th International Conference on Interaction Design and Children, pp. 418–421. ACM (2015)Google Scholar
  5. 5.
    van Overbeeke, B.: Service Robot Amigo at the RoboCup Dutch Open 2012 (2011). 19 May 2017
  6. 6.
    Brachman, R.J., Levesque, H.J.: Knowledge Representation and Reasoning. Morgan Kaufmann Series in Artificial Intelligence. Morgan Kaufmann, Los Altos (2003)zbMATHGoogle Scholar
  7. 7.
    Gebser, M., Kaminski, R., Kaufmann, B., Schaub, T.: Clingo = ASP + Control: Extended Report (2014). 10 Oct 2017
  8. 8.
    Gebser, M., Janhunen, T., Jost, H., Kaminski, R., Schaub, T.: ASP solving for expanding universes. In: Calimeri, F., Ianni, G., Truszczynski, M. (eds.) Proceedings of the 13th International Conference on Logic Programming and Non-monotonic Reasoning, pp. 354–367. Springer, Heidelberg (2015). Scholar
  9. 9.
    Skubch, H.: Modelling and Controlling of Behaviour for Autonomous Mobile Robots, 1st edn. Springer Vieweg, Heidelberg (2013). Scholar
  10. 10.
    Opfer, S., Niemczyk, S., Geihs, K.: Multi-agent plan verification with answer set programming. In: Aßmann, U., Brugali, D., Piechnick, C. (eds.) Proceedings of the Third Workshop on Model-Driven Robot Software Engineering. ACM (2016)Google Scholar
  11. 11.
    Gelfond, M., Kahl, Y.: Knowledge Representation, Reasoning, and the Design of Intelligent Agents: The Answer-Set Programming Approach. Cambridge University Press, Cambridge (2014)CrossRefGoogle Scholar
  12. 12.
    Opfer, S., Jakob, S., Geihs, K.: Reasoning for autonomous agents in dynamic domains. In: van den Herik, J., Rocha, A.P., Filipe, J. (eds.) Proceedings of the 9th International Conference on Agents and Artificial Intelligence, pp. 340–351. ScitePress Digital Library (2017)Google Scholar
  13. 13.
    Randell, D.A., Cui, Z., Cohn, A.G.: A spatial logic based on regions and connection. In: Nebel, B., Rich, C., Swartout, W. (eds.) Proceedings of the 3rd International Conference on Principles of Knowledge Representation and Reasoning, vol. 92, San Francisco, CA, USA, pp. 165–176. Morgan Kaufmann (1992)Google Scholar
  14. 14.
    Skubch, H., Saur, D., Geihs, K.: Resolving conflicts in highly reactive teams. In: Luttenberger, N., Peters, H. (eds.) 17th GI/ITG Conference on Communication in Distributed Systems (KiVS 2011). OpenAccess Series in Informatics (OASIcs), vol. 17, pp. 170–175, Dagstuhl, Germany, Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik (2011)Google Scholar
  15. 15.
    Skubch, H., Wagner, M., Reichle, R., Geihs, K.: A modelling language for cooperative plans in highly dynamic domains. In: van de Molengraft, M., Zweigle, O. (eds.) Special Issue on Advances in Intelligent Robot Design for the Robocup Middle Size League, vol. 21, pp. 423–433. Elsevier (2011)CrossRefGoogle Scholar
  16. 16.
    Gat, E.: On three-layer architectures. In: Kortenkamp, D., Bonasso, R.P., Murphy, R. (eds.) Artificial Intelligence and Mobile Robots: Case Studies of Successful Robot Systems, pp. 195–210. MIT Press, Cambridge (1998)Google Scholar
  17. 17.
    Brewka, G., Eiter, T., Truszczyński, M.: Answer set programming at a glance. Commun. ACM 54, 92–103 (2011)CrossRefGoogle Scholar
  18. 18.
    Leone, N., Pfeifer, G., Faber, W., Eiter, T., Gottlob, G., Perri, S., Scarcello, F.: The DLV system for knowledge representation and reasoning. ACM Trans. Comput. Logic 7, 499–562 (2006)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Eiter, T., Ianni, G., Krennwallner, T.: Answer set programming: a primer. In: Tessaris, S., Franconi, E., Eiter, T., Gutierrez, C., Handschuh, S., Rousset, M.-C., Schmidt, R.A. (eds.) Reasoning Web 2009. LNCS, vol. 5689, pp. 40–110. Springer, Heidelberg (2009). Scholar
  20. 20.
    Gebser, M., Grote, T., Kaminski, R., Obermeier, P., Sabuncu, O., Schaub, T.: Answer set programming for stream reasoning. In: Eiter, T., McIlraith, S. (eds.) Proceedings of the Thirteenth International Conference on Principles of Knowledge Representation and Reasoning (KR 2012), vol. 13, pp. 613–617. AAAI Press (2012)Google Scholar
  21. 21.
    Gatsoulis, Y., Alomari, M., Burbridge, C., Dondrup, C., Duckworth, P., Lightbody, P., Hanheide, M., Hawes, N., Hogg, D.C., Cohn, A.G.: QSRlib: a software library for online acquisition of qualitative spatial relations from video. In: Bredeweg, B., Kansou, K., Klenk, M. (eds.) Proceedings of the 29th International Workshop on Qualitative Reasoning, pp. 36–41 (2016)Google Scholar
  22. 22.
    Witsch, A.: Decision Making for Teams of Mobile Robots. Dissertation, University of Kassel, Kassel, Germany (2016)Google Scholar
  23. 23.
    Sharir, M.: A strong-connectivity algorithm and its applications in data flow analysis. Comput. Math. Appl. 7, 67–72 (1981)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Schaub, T.: Module Composition, 13 October 2016. 9 May 2017
  25. 25.
    Foote, T., Wise, M.: TurtleBot Website (2010). 9 May 2017
  26. 26.
    Heintz, F., Kvarnström, J., Doherty, P.: Bridging the sense-reasoning gap: DyKnow - stream-based middleware for knowledge processing. Adv. Eng. Inform. 24, 14–26 (2010)CrossRefGoogle Scholar
  27. 27.
    Tenorth, M., Beetz, M.: KnowRob: a knowledge processing infrastructure for cognition-enabled robots. Int. J. Robot. Res. 32, 566–590 (2013)CrossRefGoogle Scholar
  28. 28.
    Erdem, E., Aker, E., Patoglu, V.: Answer set programming for collaborative housekeeping robotics: representation, reasoning, and execution. J. Intell. Serv. Robots 5, 275–291 (2012)CrossRefGoogle Scholar
  29. 29.
    Speer, R., Havasi, C.: ConceptNet 5: a large semantic network for relational knowledge. In: Gurevych, I., Kim, J. (eds.) The People’s Web Meets NLP. Theory and Applications of Natural Language Processing, pp. 161–176. Springer, Heidelberg (2013). Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Distributed Systems Research GroupUniversity of KasselKasselGermany

Personalised recommendations