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Negative Updating Combined with Opinion Pooling in the Best-of-n Problem in Swarm Robotics

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Negative Updating Combined with Opinion Pooling in the Best-of-n Problem in Swarm Robotics. / Lee, Chanelle; Lawry, Jonathan; Winfield, Alan.

Swarm Intelligence: 11th International Conference, ANTS 2018 Proceedings (Lecture Notes in Computer Science), October 29-31, 2018. Rome, Italy. Springer Nature, 2018. p. 97-108 (Lecture Notes in Computer Science; Vol. 11172).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Lee, C, Lawry, J & Winfield, A 2018, Negative Updating Combined with Opinion Pooling in the Best-of-n Problem in Swarm Robotics. in Swarm Intelligence: 11th International Conference, ANTS 2018 Proceedings (Lecture Notes in Computer Science), October 29-31, 2018. Rome, Italy. Lecture Notes in Computer Science, vol. 11172, Springer Nature, pp. 97-108, Eleventh International Conference on Swarm Intelligence (ANTS 2018), Rome, Italy, 29/10/18. https://doi.org/10.1007/978-3-030-00533-7_8

APA

Lee, C., Lawry, J., & Winfield, A. (2018). Negative Updating Combined with Opinion Pooling in the Best-of-n Problem in Swarm Robotics. In Swarm Intelligence: 11th International Conference, ANTS 2018 Proceedings (Lecture Notes in Computer Science), October 29-31, 2018. Rome, Italy (pp. 97-108). (Lecture Notes in Computer Science; Vol. 11172). Springer Nature. https://doi.org/10.1007/978-3-030-00533-7_8

Vancouver

Lee C, Lawry J, Winfield A. Negative Updating Combined with Opinion Pooling in the Best-of-n Problem in Swarm Robotics. In Swarm Intelligence: 11th International Conference, ANTS 2018 Proceedings (Lecture Notes in Computer Science), October 29-31, 2018. Rome, Italy. Springer Nature. 2018. p. 97-108. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-030-00533-7_8

Author

Lee, Chanelle ; Lawry, Jonathan ; Winfield, Alan. / Negative Updating Combined with Opinion Pooling in the Best-of-n Problem in Swarm Robotics. Swarm Intelligence: 11th International Conference, ANTS 2018 Proceedings (Lecture Notes in Computer Science), October 29-31, 2018. Rome, Italy. Springer Nature, 2018. pp. 97-108 (Lecture Notes in Computer Science).

Bibtex

@inproceedings{682529c33e4347dea44d8484f8a756d6,
title = "Negative Updating Combined with Opinion Pooling in the Best-of-n Problem in Swarm Robotics",
abstract = "There is a need for effective collective decision making in decentralised multi-agent and robotic systems. This paper introduces a novel approach to the best-of-n decision problem with large n. It utilises negative feedback obtained from direct pairwise comparison of options and evidence preserving opinion pooling. We present agent-based simulation experiments that explore the effects of pool size and the number of options on the speed of consensus. Robotic simulation experiments are then used to investigate the potential of the approach as a method for solving the best-of-n decision problem in swarm robotic applications. Overall, the results suggest that the proposed approach is highly scalable with regards to n.",
author = "Chanelle Lee and Jonathan Lawry and Alan Winfield",
year = "2018",
month = "10",
day = "25",
doi = "10.1007/978-3-030-00533-7_8",
language = "English",
isbn = "9783030005320",
series = "Lecture Notes in Computer Science",
publisher = "Springer Nature",
pages = "97--108",
booktitle = "Swarm Intelligence",
address = "United Kingdom",

}

RIS - suitable for import to EndNote

TY - GEN

T1 - Negative Updating Combined with Opinion Pooling in the Best-of-n Problem in Swarm Robotics

AU - Lee, Chanelle

AU - Lawry, Jonathan

AU - Winfield, Alan

PY - 2018/10/25

Y1 - 2018/10/25

N2 - There is a need for effective collective decision making in decentralised multi-agent and robotic systems. This paper introduces a novel approach to the best-of-n decision problem with large n. It utilises negative feedback obtained from direct pairwise comparison of options and evidence preserving opinion pooling. We present agent-based simulation experiments that explore the effects of pool size and the number of options on the speed of consensus. Robotic simulation experiments are then used to investigate the potential of the approach as a method for solving the best-of-n decision problem in swarm robotic applications. Overall, the results suggest that the proposed approach is highly scalable with regards to n.

AB - There is a need for effective collective decision making in decentralised multi-agent and robotic systems. This paper introduces a novel approach to the best-of-n decision problem with large n. It utilises negative feedback obtained from direct pairwise comparison of options and evidence preserving opinion pooling. We present agent-based simulation experiments that explore the effects of pool size and the number of options on the speed of consensus. Robotic simulation experiments are then used to investigate the potential of the approach as a method for solving the best-of-n decision problem in swarm robotic applications. Overall, the results suggest that the proposed approach is highly scalable with regards to n.

U2 - 10.1007/978-3-030-00533-7_8

DO - 10.1007/978-3-030-00533-7_8

M3 - Conference contribution

SN - 9783030005320

T3 - Lecture Notes in Computer Science

SP - 97

EP - 108

BT - Swarm Intelligence

PB - Springer Nature

ER -