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2020 – today
- 2024
[i7]Michael Crosscombe, Ilya Horiguchi, Norihiro Maruyama, Shigeto Dobata, Takashi Ikegami:
A Simulation Environment for the Neuroevolution of Ant Colony Dynamics. CoRR abs/2406.13147 (2024)- 2023
- [i6]Michael Crosscombe, Jonathan Lawry:
The Benefits of Interaction Constraints in Distributed Autonomous Systems. CoRR abs/2306.01179 (2023) - 2022
- [c9]Michael Crosscombe, Jonathan Lawry:
The Benefits of Interaction Constraints in Distributed Autonomous Systems. DARS 2022: 14-27 - 2021
- [j2]Michael Crosscombe
, Jonathan Lawry:
Collective preference learning in the best-of-n problem. Swarm Intell. 15(1-2): 145-170 (2021) - [c8]Michael Crosscombe, Jonathan Lawry:
The Impact of Network Connectivity on Collective Learning. DARS 2021: 82-94 - [c7]Zixuan Liu, Michael Crosscombe, Jonathan Lawry:
Imprecise Fusion Operators for Collective Learning. ALIFE 2021: 77 - [c6]Emma Barden, Michael Crosscombe, Kevin Galvin, Chris Harding, Angus Johnson, Thomas E. Kent, Ben Pritchard, Arthur Richards, Debora Zanatto:
Academic and Industrial Partnerships in the Research and Development of Hybrid Autonomous Systems: Challenges, Tools and Methods. MESAS 2021: 478-493 - [i5]Michael Crosscombe, Jonathan Lawry:
The Impact of Network Connectivity on Collective Learning. CoRR abs/2106.00655 (2021) - 2020
- [i4]Jonathan Lawry, Michael Crosscombe, David Harvey:
Distributed Possibilistic Learning in Multi-Agent Systems. CoRR abs/2001.07145 (2020)
2010 – 2019
- 2019
- [c5]Michael Crosscombe, Jonathan Lawry:
Evidence Propagation and Consensus Formation in Noisy Environments. AAMAS 2019: 1904-1906 - [c4]Jonathan Lawry, Michael Crosscombe, David Harvey:
Epistemic Sets Applied to Best-of-n Problems. ECSQARU 2019: 301-312 - [c3]Michael Crosscombe, Jonathan Lawry, Palina Bartashevich:
Evidence Propagation and Consensus Formation in Noisy Environments. SUM 2019: 310-323 - [i3]Michael Crosscombe, Jonathan Lawry:
Evidence Propagation and Consensus Formation in Noisy Environments. CoRR abs/1905.04840 (2019) - 2018
- [b1]Michael Crosscombe:
Exploiting vagueness for multi-agent consensus. University of Bristol, UK, 2018 - [c2]Iván Palomares, Michael Crosscombe, Zhen-Song Chen, Jonathan Lawry:
Dual Consensus Measure for Multi-perspective Multi-criteria Group Decision Making. SMC 2018: 3313-3318 - 2017
- [c1]Michael Crosscombe, Jonathan Lawry, Sabine Hauert, Martin E. Homer:
Robust distributed decision-making in robot swarms: Exploiting a third truth state. IROS 2017: 4326-4332 - 2016
- [j1]Michael Crosscombe, Jonathan Lawry:
A model of multi-agent consensus for vague and uncertain beliefs. Adapt. Behav. 24(4): 249-260 (2016) - [i2]Michael Crosscombe, Jonathan Lawry:
Exploiting Vagueness for Multi-Agent Consensus. CoRR abs/1607.05540 (2016) - [i1]Michael Crosscombe, Jonathan Lawry:
A Model of Multi-Agent Consensus for Vague and Uncertain Beliefs. CoRR abs/1612.03433 (2016)
Coauthor Index
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