Interfacing Belief-Desire-Intention Agent Systems with Geometric Reasoning for Robotics and Manufacturing

  • Lavindra de SilvaEmail author
  • Felipe Meneguzzi
  • David Sanderson
  • Jack C. Chaplin
  • Otto J. Bakker
  • Nikolas Antzoulatos
  • Svetan Ratchev
Part of the Studies in Computational Intelligence book series (SCI, volume 640)


Unifying the symbolic and geometric representations and algorithms used in AI and robotics is an important challenge for both fields. We take a small step in this direction by presenting an interface between geometric reasoning and a popular class of agent systems, in a way that uses some of the agent’s available constructs and semantics. We then describe how certain kinds of information can be extracted from the geometric model of the world and used in agent reasoning. We motivate our concepts and algorithms within the context of a real-world production system.


BDI agents Geometric reasoning Robotics Manufacturing system 



We thank Amit Kumar Pandey and the reviewers for useful feedback. Felipe thanks CNPq for support within grant no. 306864/2013-4 under the PQ fellowship and 482156/2013-9 under the Universal project programs. The other authors are grateful for support from the Evolvable Assembly Systems EPSRC project (EP/K018205/1), and the PRIME EU FP7 project (Grant Agreement: 314762).


  1. 1.
    Rao, A.S.: AgentSpeak(L): BDI agents speak out in a logical computable language. In: Proceedings of the MAAMAW Workshop, pp. 42–55 (1996)Google Scholar
  2. 2.
    de Silva, L., Pandey, A.K. Alami, R.: An interface for interleaved symbolic-geometric planning and backtracking. In: IROS, pp. 232–239 (2013)Google Scholar
  3. 3.
    Srivastava, S., Fang, E., Riano, L., Chitnis, R., Russell, S., Abbeel, P.: Combined task and motion planning through an extensible planner-independent interface layer, ICRA, pp. 639–646 (2014)Google Scholar
  4. 4.
    Lagriffoul, F., Dimitrov, D., Saffiotti, A., Karlsson, L.: Constraint propagation on interval bounds for dealing with geometric backtracking, IROS, pp. 957–964 (2012)Google Scholar
  5. 5.
    Erdem, E., Haspalamutgil, K., Palaz, C., Patoglu, V., Uras, T.: Combining high-level causal reasoning with low-level geometric reasoning and motion planning for robotic manipulation, ICRA, pp. 4575–4581, (2011)Google Scholar
  6. 6.
    Plaku, E. Hager, G.D.: Sampling-based motion and symbolic action planning with geometric and differential constraints, ICRA, pp. 5002–5008 (2010)Google Scholar
  7. 7.
    Dornhege, C., Eyerich, P., Keller, T., Trüg, S., Brenner, M. Nebel, B.: Semantic attachments for domain-independent planning systems, ICAPS, pp. 114–121 (2009)Google Scholar
  8. 8.
    Gaschler, A., Kessler, I., Petrick, R., Knoll, A.: Extending the knowledge of volumes approach to robot task planning with efficient geometric predicates. ICRA, (2015) (To appear)Google Scholar
  9. 9.
    Kaelbling, L.P., Lozano-Pérez, T.: Integrated task and motion planning in belief space. IJRR 32(9–10), 1194–1227 (2013)Google Scholar
  10. 10.
    LaValle, S.M.: Planning Algorithms. Cambridge University Press (2006)Google Scholar
  11. 11.
    Antzoulatos, N., Castro, E., de Silva, L., Ratchev, S.: Interfacing agents with an industrial assembly system for “plug and produce”. In AAMAS, pp. 1957–1958 (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Lavindra de Silva
    • 1
    Email author
  • Felipe Meneguzzi
    • 2
  • David Sanderson
    • 1
  • Jack C. Chaplin
    • 1
  • Otto J. Bakker
    • 1
  • Nikolas Antzoulatos
    • 1
  • Svetan Ratchev
    • 1
  1. 1.Faculty of Engineering, Institute for Advanced ManufacturingUniversity of NottinghamNottinghamUK
  2. 2.Pontifical Catholic University of Rio Grande Do SulPorto AlegreBrazil

Personalised recommendations