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providing the underlying solver with more choices. In
theory, this should make it possible for the agents to
avoid collisions more easily. In our experiments, we
found that this rarely happens and that it is more ben-
eficial to provide the solver with just one random path
making the relaxed instances simpler for the cost of
possibly having to solve more relaxations. Thus, we
showed that the original approach is justified, a result
that is lacking in the original study. On the other hand,
we also showed that providing the agents with more
possible paths leads more often to an optimal solution
when using one of the suboptimal strategies.
ACKNOWLEDGEMENTS
Research is supported by project P103-19-02183S of
the Czech Science Foundation, the Czech-USA Co-
operative Scientific Research Project LTAUSA19072,
and DFG grant SCHA 550/15, Germany.
REFERENCES
Gebser, M., Kaminski, R., Kaufmann, B., Lindauer, M., Os-
trowski, M., Romero, J., Schaub, T., and Thiele, S.
(2019). Potassco User Guide, 2.2.0.
Gebser, M., Obermeier, P., Otto, T., Schaub, T., Sabuncu,
O., Nguyen, V., and Son, T. (2018a). Experiment-
ing with robotic intra-logistics domains. Theory and
Practice of Logic Programming, 18(3-4):502–519.
Gebser, M., Obermeier, P., Schaub, T., Ratsch-Heitmann,
M., and Runge, M. (2018b). Routing driverless trans-
port vehicles in car assembly with answer set pro-
gramming. Theory and Practice of Logic Program-
ming, 18(3-4):520–534.
G
´
omez, R. N., Hern
´
andez, C., and Baier, J. A. (2021). A
compact answer set programming encoding of multi-
agent pathfinding. IEEE Access, 9:26886–26901.
H
¨
acker, C., Bouros, P., Chondrogiannis, T., and Althaus,
E. (2021). Most diverse near-shortest paths. In 29th
International Conference on Advances in Geographic
Information Systems, pages 229–239. ACM.
Hanaka, T., Kobayashi, Y., Kurita, K., Lee, S. W., and
Otachi, Y. (2021). Computing diverse shortest paths
efficiently: A theoretical and experimental study.
CoRR, abs/2112.05403.
Hus
´
ar, M., Svancara, J., Obermeier, P., Bart
´
ak, R., and
Schaub, T. (2022). Reduction-based solving of
multi-agent pathfinding on large maps using graph
pruning. In 21st International Conference on Au-
tonomous Agents and Multiagent Systems, pages 624–
632. IFAAMAS.
Kaminski, R., Romero, J., Schaub, T., and Wanko, P.
(2022). How to build your own ASP-based sys-
tem?! Theory and Practice of Logic Programming,
23(1):299-361.
Kornhauser, D., Miller, G. L., and Spirakis, P. G. (1984).
Coordinating pebble motion on graphs, the diame-
ter of permutation groups, and applications. In 25th
Annual Symposium on Foundations of Computer Sci-
ence, pages 241–250.
Li, J., Tinka, A., Kiesel, S., Durham, J. W., Kumar, T.
K. S., and Koenig, S. (2020). Lifelong multi-agent
path finding in large-scale warehouses. In 19th Inter-
national Conference on Autonomous Agents and Mul-
tiagent Systems, pages 1898–1900. IFAAMAS.
Nguyen, V., Obermeier, P., Son, T. C., Schaub, T., and Yeoh,
W. (2017). Generalized target assignment and path
finding using answer set programming. In 26th In-
ternational Joint Conference on Artificial Intelligence,
pages 1216–1223. ijcai.org.
Ratner, D. and Warmuth, M. K. (1990). NxN puzzle
and related relocation problem. J. Symb. Comput.,
10(2):111–138.
Sharon, G., Stern, R., Goldenberg, M., and Felner, A.
(2011). The increasing cost tree search for optimal
multi-agent pathfinding. In 22nd International Joint
Conference on Artificial Intelligence, pages 662–667.
Silver, D. (2005). Cooperative pathfinding. In Artifi-
cial Intelligence and Interactive Digital Entertain-
ment, pages 117–122.
Stern, R., Sturtevant, N. R., Felner, A., Koenig, S., Ma, H.,
Walker, T. T., Li, J., Atzmon, D., Cohen, L., Kumar,
T. K. S., Bart
´
ak, R., and Boyarski, E. (2019). Multi-
agent pathfinding: Definitions, variants, and bench-
marks. In 12th International Symposium on Combi-
natorial Search, pages 151–159. AAAI Press.
Surynek, P. (2019). Unifying search-based and
compilation-based approaches to multi-agent
path finding through satisfiability modulo theories.
In 28th International Joint Conference on Artificial
Intelligence, pages 1177–1183. ijcai.org.
Svancara, J. and Bart
´
ak, R. (2019). Combining strengths of
optimal multi-agent path finding algorithms. In 11th
International Conference on Agents and Artificial In-
telligence, pages 226–231. SciTePress.
Yu, J. and LaValle, S. M. (2013). Structure and intractability
of optimal multi-robot path planning on graphs. In
27th AAAI Conference on Artificial Intelligence.
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