Michael Crosscombe

Orcid: 0000-0002-5287-0249

According to our database1, Michael Crosscombe authored at least 14 papers between 2016 and 2022.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

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Bibliography

2022
The Benefits of Interaction Constraints in Distributed Autonomous Systems.
Proceedings of the Distributed Autonomous Robotic Systems - 16th International Symposium, 2022

2021
Collective preference learning in the best-of-n problem.
Swarm Intell., 2021

Academic and Industrial Partnerships in the Research and Development of Hybrid Autonomous Systems: Challenges, Tools and Methods.
Proceedings of the Modelling and Simulation for Autonomous Systems, 2021

Imprecise Fusion Operators for Collective Learning.
Proceedings of the 2021 Conference on Artificial Life, 2021

The Impact of Network Connectivity on Collective Learning.
Proceedings of the Distributed Autonomous Robotic Systems - 15th International Symposium, 2021

2020
Distributed Possibilistic Learning in Multi-Agent Systems.
CoRR, 2020

2019
Evidence Propagation and Consensus Formation in Noisy Environments.
Proceedings of the Scalable Uncertainty Management - 13th International Conference, 2019

Epistemic Sets Applied to Best-of-<i>n</i> Problems.
Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2019

Evidence Propagation and Consensus Formation in Noisy Environments.
Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019

2018
Exploiting vagueness for multi-agent consensus.
PhD thesis, 2018

Dual Consensus Measure for Multi-perspective Multi-criteria Group Decision Making.
Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, 2018

2017
Robust distributed decision-making in robot swarms: Exploiting a third truth state.
Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2017

2016
Exploiting Vagueness for Multi-Agent Consensus.
CoRR, 2016

A model of multi-agent consensus for vague and uncertain beliefs.
Adapt. Behav., 2016


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