18 citations · 20 across the 5 of their papers we have counts for
5 papers · 1 filter
Imprecise Belief Fusion Improves Multi-agent Social Learning
Zixuan Liu, Jonathan Lawry, Michael Crosscombe
In social learning, agents learn not only from direct evidence but also through interactions with their peers. We investigate the role of imprecision in such interactions and ask w…
The Benefits of Interaction Constraints in Distributed Autonomous Systems
Michael Crosscombe, Jonathan Lawry
The design of distributed autonomous systems often omits consideration of the underlying network dynamics. Recent works in multi-agent systems and swarm robotics alike have highlig…
The Impact of Network Connectivity on Collective Learning
Michael Crosscombe, Jonathan Lawry
In decentralised autonomous systems it is the interactions between individual agents which govern the collective behaviours of the system. These local-level interactions are themse…
Distributed Possibilistic Learning in Multi-Agent Systems
Jonathan Lawry, Michael Crosscombe, David Harvey
Possibility theory is proposed as an uncertainty representation framework for distributed learning in multi-agent systems and robot swarms. In particular, we investigate its applic…
A Model of Multi-Agent Consensus for Vague and Uncertain Beliefs
Michael Crosscombe, Jonathan Lawry
Consensus formation is investigated for multi-agent systems in which agents' beliefs are both vague and uncertain. Vagueness is represented by a third truth state meaning \emph{bor…