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Explanation Generation for Multi-Modal Multi-Agent Path Finding with Optimal Resource Utilization using Answer Set Programming

Published online by Cambridge University Press:  22 September 2020

AYSU BOGATARKAN  and
ESRA ERDEM Open the ORCID record for ESRA ERDEM [Opens in a new window]
AYSU BOGATARKAN
Affiliation:
Sabanci University, Faculty of Engineering and Natural Sciences, Istanbul, Turkey (e-mail: aysubogatarkan@sabanciuniv.edu, esraerdem@sabanciuniv.edu)
ESRA ERDEM
Affiliation:
Sabanci University, Faculty of Engineering and Natural Sciences, Istanbul, Turkey (e-mail: aysubogatarkan@sabanciuniv.edu, esraerdem@sabanciuniv.edu)
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Abstract

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The multi-agent path finding (MAPF) problem is a combinatorial search problem that aims at finding paths for multiple agents (e.g., robots) in an environment (e.g., an autonomous warehouse) such that no two agents collide with each other, and subject to some constraints on the lengths of paths. We consider a general version of MAPF, called mMAPF, that involves multi-modal transportation modes (e.g., due to velocity constraints) and consumption of different types of resources (e.g., batteries). The real-world applications of mMAPF require flexibility (e.g., solving variations of mMAPF) as well as explainability. Our earlier studies on mMAPF have focused on the former challenge of flexibility. In this study, we focus on the latter challenge of explainability, and introduce a method for generating explanations for queries regarding the feasibility and optimality of solutions, the nonexistence of solutions, and the observations about solutions. Our method is based on answer set programming.

Information

Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 20 , Issue 6: 36th International Conference on Logic Programming Special Issue II , November 2020 , pp. 974 - 989
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Copyright
© The Author(s), 2020. Published by Cambridge University Press

Footnotes

*

This work is supported by Tubitak Grant 118E931.

References

Almagor, S. and Lahijanian, M. 2020. Explainable multi agent path finding. In Proc. of AAMAS. 34–42.Google Scholar
Bogatarkan, A., Erdem, E., Kleiner, A., and Patoglu, V. 2020. Multi-modal multi-agent path finding with optimal resource utilization. In Proceedings of 5th International Conference on the Industry 4.0 Model for Advanced Manufacturing. 313–324.Google Scholar
Bogatarkan, A., Patoglu, V., and Erdem, E. 2019. A declarative method for dynamic multi-agent path finding. In Proceedings of the 5th Global Conference on Artificial Intelligence. 54–67.Google Scholar
Brain, M., Gebser, M., Pührer, J., Schaub, T., Tompits, H., and Woltran, S. 2007. “that is illogical captain!”–the debugging support tool spock for answer-set programs: System description.Google Scholar
Cabalar, P. and Fandinno, J. 2016. Justifications for programs with disjunctive and causal-choice rules. Theory Pract. Log. Program. 16, 5-6, 587–603.Google Scholar
Cabalar, P., Fandinno, J., and Fink, M. 2014. Causal graph justifications of logic programs. Theory Pract. Log. Program. 14, 4-5, 603–618.Google Scholar
Calimeri, F., Faber, W., Gebser, M., Ianni, G., Kaminski, R., Krennwallner, T., Leone, N., Ricca, F., and Schaub, T. 2020. ASP-Core-2 Input Language Format. Theory Pract. Log. Program. 20, 2, 294–309.Google Scholar
Chouhan, S. S. and Niyogi, R. 2015. DMAPP: A distributed multi-agent path planning algorithm. In Proc. of AI. 123–135.Google Scholar
Damásio, C. V., Analyti, A., and Antoniou, G. 2013. Justifications for logic programming. In Proc. of LPNMR. 530–542.Google Scholar
Dijkstra, E. W. 1959. A note on two problems in connexion with graphs. Numer. Math. 1, 1, 269–271.CrossRefGoogle Scholar
Dodaro, C., Gasteiger, P., Reale, K., Ricca, F., and Schekotihin, K. 2019. Debugging non-ground ASP programs: Technique and graphical tools. Theory Pract. Log. Program. 19, 2, 290–316.CrossRefGoogle Scholar
Dresner, K. M. and Stone, P. 2008. A multiagent approach to autonomous intersection management. J. Artif. Intell. Res. (JAIR) 31, 591–695.Google Scholar
Eifler, R., Cashmore, M., Hoffmann, J., Magazzeni, D., and Steinmetz, M. 2020. A new approach to plan-space explanation: Analyzing plan-property dependencies in oversubscription planning. In Proc. of AAAI. 9818–9826.Google Scholar
Erdem, E., Kisa, D. G., Oztok, U., and Schueller, P. 2013. A general formal framework for pathfinding problems with multiple agents. In Proc. of AAAI.Google Scholar
Erdem, E. and Öztok, U. 2015. Generating explanations for biomedical queries. Theory Pract. Log. Program. 15, 1, 35–78.Google Scholar
Fandinno, J. and Schulz, C. 2019. Answering the “why” in answer set programming - A survey of explanation approaches. Theory Pract. Log. Program. 19, 2, 114–203.Google Scholar
Gebser, M., Pührer, J., Schaub, T., and Tompits, H. 2008. A meta-programming technique for debugging answer-set programs. In Proc. of AAAI. 448–453.Google Scholar
Gelfond, M. and Lifschitz, V. 1988. The stable model semantics for logic programming. In Proceedings of International Logic Programming Conference and Symposium. 1070–1080.Google Scholar
Gelfond, M. and Lifschitz, V. 1991. Classical negation in logic programs and disjunctive databases. New Generation Computing 9, 365–385.CrossRefGoogle Scholar
Jansen, R. and Sturtevant, N. 2008. A new approach to cooperative pathfinding. In Proc. of AAMAS. 1401–1404.Google Scholar
Lifschitz, V. 2002. Answer set programming and plan generation. Artificial Intelligence 138, 39–54.CrossRefGoogle Scholar
Luna, R. and Bekris, K. E. 2011. Efficient and complete centralized multi-robot path planning. In Proc. of IROS. 3268–3275.Google Scholar
Marek, V. and Truszczyński, M. 1999. Stable models and an alternative logic programming paradigm. In The Logic Programming Paradigm: a 25-Year Perspective. Springer Verlag, 375–398.Google Scholar
Niemelä, I. 1999. Logic programs with stable model semantics as a constraint programming paradigm. Annals of Mathematics and Artificial Intelligence 25, 241–273.Google Scholar
Oetsch, J., Pührer, J., and Tompits, H. 2010. Catching the ouroboros: On debugging non-ground answer-set programs. Theory Pract. Log. Program. 10, 4-6, 513–529.CrossRefGoogle Scholar
Pontelli, E., Son, T. C., and El-Khatib, O. 2009. Justifications for logic programs under answer set semantics. Theory Pract. Log. Program. 9, 1, 1–56.Google Scholar
Ratner, D. and Warmuth, M. K. 1986. Finding a shortest solution for the n \[ \times \] n extension of the 15-puzzle is intractable. In Proc. of AAAI. 168–172.Google Scholar
Schulz, C. and Toni, F. 2013. Aba-based answer set justification. Theory Pract. Log. Program. 13, 4-5-Online-Supplement.Google Scholar
Schulz, C. and Toni, F. 2016. Justifying answer sets using argumentation. Theory Pract. Log. Program. 16, 1, 59–110.Google Scholar
Sharon, G., Stern, R., Felner, A., and Sturtevant, N. R. 2015. Conflict-based search for optimal multi-agent pathfinding. Artif. Intell. 219, 40–66.CrossRefGoogle Scholar
Silver, D. 2005. Cooperative pathfinding. In Proc. of AIIDE. 117–122.Google Scholar
Smith, D. E. 2012. Planning as an iterative process. In Proc. of AAAI, Hoffmann, J. and Selman, B., Eds.Google Scholar
Stern, R., Sturtevant, N. R., Felner, A., Koenig, S., Ma, H., Walker, T. T., Li, J., Atzmon, D., Cohen, L., Kumar, T. K. S., Barták, R., and Boyarski, E. 2019. Multi-agent pathfinding: Definitions, variants, and benchmarks. In Proc. of SOCS. 151–159.Google Scholar
Surynek, P. 2012. On propositional encodings of cooperative path-finding. In Proc. of ICTAI. 524–531.Google Scholar
Wang, K.-H. C. and Botea, A. 2008. Fast and memory-efficient multi-agent pathfinding. In Proc. of ICAPS. 380–387.Google Scholar
Yu, J. and LaValle, S. M. 2013. Planning optimal paths for multiple robots on graphs. In Proc. of ICRA. 3612–3617.Google Scholar