Hostname: page-component-7dd5485656-wxk4p Total loading time: 0 Render date: 2025-10-30T06:27:58.940Z Has data issue: false hasContentIssue false
  • English
  • Français

Experimenting with robotic intra-logistics domains

Published online by Cambridge University Press:  10 August 2018

MARTIN GEBSER ,
PHILIPP OBERMEIER ,
THOMAS OTTO ,
TORSTEN SCHAUB Open the ORCID record for TORSTEN SCHAUB [Opens in a new window] ,
ORKUNT SABUNCU ,
VAN NGUYEN  and
TRAN CAO SON
MARTIN GEBSER
Affiliation:
University of Potsdam, Germany
PHILIPP OBERMEIER
Affiliation:
University of Potsdam, Germany
THOMAS OTTO
Affiliation:
University of Potsdam, Germany
TORSTEN SCHAUB
Affiliation:
University of Potsdam, Germany
ORKUNT SABUNCU
Affiliation:
TED University, Ankara, Turkey
VAN NGUYEN
Affiliation:
New Mexico State University, Las Cruces, USA
TRAN CAO SON
Affiliation:
New Mexico State University, Las Cruces, USA
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

We introduce the asprilo1 framework to facilitate experimental studies of approaches addressing complex dynamic applications. For this purpose, we have chosen the domain of robotic intra-logistics. This domain is not only highly relevant in the context of today's fourth industrial revolution but it moreover combines a multitude of challenging issues within a single uniform framework. This includes multi-agent planning, reasoning about action, change, resources, strategies, etc. In return, asprilo allows users to study alternative solutions as regards effectiveness and scalability. Although asprilo relies on Answer Set Programming and Python, it is readily usable by any system complying with its fact-oriented interface format. This makes it attractive for benchmarking and teaching well beyond logic programming. More precisely, asprilo consists of a versatile benchmark generator, solution checker and visualizer as well as a bunch of reference encodings featuring various ASP techniques. Importantly, the visualizer's animation capabilities are indispensable for complex scenarios like intra-logistics in order to inspect valid as well as invalid solution candidates. Also, it allows for graphically editing benchmark layouts that can be used as a basis for generating benchmark suites.

Information

Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 18 , Special Issue 3-4: 34th International Conference on Logic Programming , July 2018 , pp. 502 - 519
Copyright
Copyright © Cambridge University Press 2018 

Footnotes

1

asprilo stands for Answer Set Programming for robotic intra-logistics.

References

Abseher, M., Gebser, M., Musliu, N., Schaub, T., and Woltran, S. 2016. Shift design with answer set programming. Fundamenta Informaticae 147, 1, 1–25.Google Scholar
Alviano, M., Dodaro, C., and Maratea, M. 2017. An advanced answer set programming encoding for nurse scheduling. In Proceedings of the Sixteenth International Conference of the Italian Association for Artificial Intelligence, Esposito, F., Basili, R., Ferilli, S., and Lisi, F., Eds. Lecture Notes in Computer Science, vol. 10640. Springer-Verlag, 468–482.Google Scholar
Banbara, M., Inoue, K., Kaufmann, B., Schaub, T., Soh, T., Tamura, N., and Wanko, P. 2016. teaspoon: Solving the curriculum-based course timetabling problems with answer set programming. In Proceedings of the Eleventh International Conference of the Practice and Theory of Automated Timetabling (PATAT'16), Burke, E., Gaspero, L. D., McCollum, B., Schaerf, A., and Özcan, E., Eds. 13–32.Google Scholar
Banbara, M., Kaufmann, B., Ostrowski, M., and Schaub, T. 2017. Clingcon: The next generation. Theory and Practice of Logic Programming 17, 4, 408–461.Google Scholar
Cholewiński, P., Marek, V., Mikitiuk, A. and Truszczyński, M. 1995. Experimenting with nonmonotonic reasoning. In Proceedings of the International Conference on Logic Programming, Sterling, L., Ed. MIT Press, 267–281.Google Scholar
Erdem, E., Kisa, D., Öztok, U., and Schüller, P. 2013. A general formal framework for pathfinding problems with multiple agents. In Proceedings of the Twenty-Seventh National Conference on Artificial Intelligence (AAAI'13), desJardins, M. and Littman, M., Eds. AAAI Press, 290–296.Google Scholar
Gebser, M., Jost, H., Kaminski, R., Obermeier, P., Sabuncu, O., Schaub, T., and Schneider, M. 2013. Ricochet robots: A transverse ASP benchmark. In Proceedings of the Twelfth International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR'13), Cabalar, P. and Son, T., Eds. Lecture Notes in Artificial Intelligence, vol. 8148. Springer-Verlag, 348–360.Google Scholar
Gebser, M., Kaminski, R., Kaufmann, B., and Schaub, T. 2018. Multi-shot ASP solving with clingo. Theory and Practice of Logic Programming. To appear.Google Scholar
Janhunen, T., Kaminski, R., Ostrowski, M., Schaub, T., Schellhorn, S., and Wanko, P. 2017. Clingo goes linear constraints over reals and integers. Theory and Practice of Logic Programming 17, 5–6, 872–888.Google Scholar
Lifschitz, V. 1999. Answer set planning. In Proceedings of the International Conference on Logic Programming (ICLP'99), de Schreye, D., Ed. MIT Press, 23–37.Google Scholar
Ma, H. and Koenig, S. 2016. Optimal target assignment and path finding for teams of agents. In Proceedings of the Fifteenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS'16), Jonker, C., Marsella, S., Thangarajah, J., and Tuyls, K., Eds. ACM Press, 1144–1152.Google Scholar
Ma, H., Li, J., Kumar, T., and Koenig, S. 2017. Lifelong multi-agent path finding for online pickup and delivery tasks. In Proceedings of the Sixteenth Conference on Autonomous Agents and MultiAgent Systems (AAMAS'17). ACM Press, 837–845.Google Scholar
Nguyen, V., Obermeier, P., Son, T., Schaub, T., and Yeoh, W. 2017. Generalized target assignment and path finding using answer set programming. In Proceedings of the Twenty-sixth International Joint Conference on Artificial Intelligence (IJCAI'17), Sierra, C., Ed. IJCAI/AAAI Press, 1216–1223.Google Scholar
Nogueira, M., Balduccini, M., Gelfond, M., Watson, R., and Barry, M. 2001. An A-prolog decision support system for the space shuttle. In Proceedings of the Third International Symposium on Practical Aspects of Declarative Languages (PADL'01), Ramakrishnan, I., Ed. Lecture Notes in Computer Science, vol. 1990. Springer-Verlag, 169–183.Google Scholar
Surynek, P. 2010. An optimization variant of multi-robot path planning is intractable. In Proceedings of the Twenty-fourth National Conference on Artificial Intelligence (AAAI'10), Fox, M. and Poole, D., Eds. AAAI Press, 1261–1263.Google Scholar
Tamura, N., Taga, A., Kitagawa, S., and Banbara, M. 2009. Compiling finite linear CSP into SAT. Constraints 14, 2, 254–272.Google Scholar
Yu, J. and LaValle, S. 2012. Multi-agent path planning and network flow. In Proceedings of the Tenth Workshop on the Algorithmic Foundations of Robotics (WAFR'12), Frazzoli, E., Lozano-Pérez, T., Roy, N., and Rus, D., Eds. Springer Tracts in Advanced Robotics, vol. 86. Springer-Verlag, 157–173.Google Scholar