Optimization of swarm behavior assisted by an automatic local proof for a pattern formation task

Mario Coppola; Guido C.H.E. de Croon

doi:10.1007/978-3-030-00533-7_10

Optimization of swarm behavior assisted by an automatic local proof for a pattern formation task

^*Corresponding author for this work

Control & Simulation

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

3 Citations (Scopus)

78 Downloads (Pure)

Abstract

In this work, we optimize the behavior of swarm agents in a pattern formation task. We start with a local behavior, expressed as a local state-action map, that has been formally proven to lead the swarm to always eventually form the desired pattern. We seek to optimize this for performance while keeping the formal proof. First, the state-action map is pruned to remove unnecessary state-action pairs, reducing the solution space. Then, the probabilities of executing the remaining actions are tuned with a genetic algorithm. The final controllers allow the swarm to form the patterns upÂ to orders of magnitude faster than with the original behavior. The optimization is found to suffer from scalability issues. These may be tackled in future work by automatically minimizing the size of the local state-action map with a further direct focus on performance.

Original language	English
Title of host publication	Swarm Intelligence - 11th International Conference, ANTS 2018, Proceedings
Editors	Marco Dorigo, Mauro Birattari, Christian Blum, Anders L. Christensen, Andreagiovanni Reina, Vito Trianni
Publisher	Springer
Pages	123-134
Number of pages	12
Volume	11172 LNCS
ISBN (Electronic)	978-3-030-00533-7
ISBN (Print)	978-3-030-00532-0
DOIs	https://doi.org/10.1007/978-3-030-00533-7_10
Publication status	Published - 1 Jan 2018
Event	ANTS 2018: 11th International Conference on Swarm Intelligence - Rome, Italy Duration: 29 Oct 2018 → 31 Oct 2018 Conference number: 11

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11172 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	ANTS 2018: 11th International Conference on Swarm Intelligence
Abbreviated title	ANTS 2018
Country/Territory	Italy
City	Rome
Period	29/10/18 → 31/10/18

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10.1007/978-3-030-00533-7_10

ANTS18_Coppolaetal_submission_camerareadyAccepted author manuscript, 688 KBLicence: CC BY-NC-ND

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Coppola, M., & de Croon, G. C. H. E. (2018). Optimization of swarm behavior assisted by an automatic local proof for a pattern formation task. In M. Dorigo, M. Birattari, C. Blum, A. L. Christensen, A. Reina, & V. Trianni (Eds.), Swarm Intelligence - 11th International Conference, ANTS 2018, Proceedings (Vol. 11172 LNCS, pp. 123-134). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11172 LNCS). Springer. https://doi.org/10.1007/978-3-030-00533-7_10

Coppola, Mario ; de Croon, Guido C.H.E. / Optimization of swarm behavior assisted by an automatic local proof for a pattern formation task. Swarm Intelligence - 11th International Conference, ANTS 2018, Proceedings. editor / Marco Dorigo ; Mauro Birattari ; Christian Blum ; Anders L. Christensen ; Andreagiovanni Reina ; Vito Trianni. Vol. 11172 LNCS Springer, 2018. pp. 123-134 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "In this work, we optimize the behavior of swarm agents in a pattern formation task. We start with a local behavior, expressed as a local state-action map, that has been formally proven to lead the swarm to always eventually form the desired pattern. We seek to optimize this for performance while keeping the formal proof. First, the state-action map is pruned to remove unnecessary state-action pairs, reducing the solution space. Then, the probabilities of executing the remaining actions are tuned with a genetic algorithm. The final controllers allow the swarm to form the patterns up{\^A} to orders of magnitude faster than with the original behavior. The optimization is found to suffer from scalability issues. These may be tackled in future work by automatically minimizing the size of the local state-action map with a further direct focus on performance.",

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Coppola, M & de Croon, GCHE 2018, Optimization of swarm behavior assisted by an automatic local proof for a pattern formation task. in M Dorigo, M Birattari, C Blum, AL Christensen, A Reina & V Trianni (eds), Swarm Intelligence - 11th International Conference, ANTS 2018, Proceedings. vol. 11172 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11172 LNCS, Springer, pp. 123-134, ANTS 2018: 11th International Conference on Swarm Intelligence, Rome, Italy, 29/10/18. https://doi.org/10.1007/978-3-030-00533-7_10

Optimization of swarm behavior assisted by an automatic local proof for a pattern formation task. / Coppola, Mario; de Croon, Guido C.H.E.
Swarm Intelligence - 11th International Conference, ANTS 2018, Proceedings. ed. / Marco Dorigo; Mauro Birattari; Christian Blum; Anders L. Christensen; Andreagiovanni Reina; Vito Trianni. Vol. 11172 LNCS Springer, 2018. p. 123-134 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11172 LNCS).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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AU - de Croon, Guido C.H.E.

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N2 - In this work, we optimize the behavior of swarm agents in a pattern formation task. We start with a local behavior, expressed as a local state-action map, that has been formally proven to lead the swarm to always eventually form the desired pattern. We seek to optimize this for performance while keeping the formal proof. First, the state-action map is pruned to remove unnecessary state-action pairs, reducing the solution space. Then, the probabilities of executing the remaining actions are tuned with a genetic algorithm. The final controllers allow the swarm to form the patterns upÂ to orders of magnitude faster than with the original behavior. The optimization is found to suffer from scalability issues. These may be tackled in future work by automatically minimizing the size of the local state-action map with a further direct focus on performance.

AB - In this work, we optimize the behavior of swarm agents in a pattern formation task. We start with a local behavior, expressed as a local state-action map, that has been formally proven to lead the swarm to always eventually form the desired pattern. We seek to optimize this for performance while keeping the formal proof. First, the state-action map is pruned to remove unnecessary state-action pairs, reducing the solution space. Then, the probabilities of executing the remaining actions are tuned with a genetic algorithm. The final controllers allow the swarm to form the patterns upÂ to orders of magnitude faster than with the original behavior. The optimization is found to suffer from scalability issues. These may be tackled in future work by automatically minimizing the size of the local state-action map with a further direct focus on performance.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Coppola M, de Croon GCHE. Optimization of swarm behavior assisted by an automatic local proof for a pattern formation task. In Dorigo M, Birattari M, Blum C, Christensen AL, Reina A, Trianni V, editors, Swarm Intelligence - 11th International Conference, ANTS 2018, Proceedings. Vol. 11172 LNCS. Springer. 2018. p. 123-134. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-00533-7_10

Optimization of swarm behavior assisted by an automatic local proof for a pattern formation task

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