“…In this paper, we extend our previous work that a gossipbased protocol can reach a consensus based on an uninorm aggregation operator [4]. The operator to aggregate positive ratings will produce an even more positive overall rating, using it to aggregate negative ratings will produce an even more negative overall rating, and will generate a neutral rating when aggregate the conflict ratings [9].…”
Section: Introductionmentioning
confidence: 90%
“…The convergence of the protocol has been proven in [4] and the convergence speed of the Algorithm 1 is dependent on the initial mean value of the opinions, moreover the convergence speed has been demonstrated relatively stable even with the increase of n. Although the protocol is guaranteed to converge, however the convergence result is not always unique. The convergence result not only depends on the pair sequence, but also depends on the distribution pattern of the initial values, please see [4] for more details.…”
Section: Theoremmentioning
confidence: 96%
“…The convergence result not only depends on the pair sequence, but also depends on the distribution pattern of the initial values, please see [4] for more details.…”
Section: Theoremmentioning
confidence: 99%
“…In our earlier paper [4], we discussed a gossip-based consensus reaching protocol based on a uninorm aggregation operator. We analyze the convergence, the speed and the randomness of this protocol.…”
Section: Related Workmentioning
confidence: 99%
“…In our earlier work [4] , we described a gossiping protocol based on the aforementioned aggregator. Suppose we are given an initial vector A = (a 1 , a 2 , · · · , a n ), where a i ∈ [0, 1], i ∈ {1, · · · , n}.…”
Gossip-based protocols for group communication have attractive scalability and reliability properties. In this paper, we extends our previous work [4]: a gossip-based protocol that enable agents to reach a consensus based on a special uninorm aggregation operator. We theoretically analyze the convergence and the randomness features of the extensions. The extended models can be more flexible in demonstrating the uncertainty and the convergence characteristics of collective decision dynamics.
“…In this paper, we extend our previous work that a gossipbased protocol can reach a consensus based on an uninorm aggregation operator [4]. The operator to aggregate positive ratings will produce an even more positive overall rating, using it to aggregate negative ratings will produce an even more negative overall rating, and will generate a neutral rating when aggregate the conflict ratings [9].…”
Section: Introductionmentioning
confidence: 90%
“…The convergence of the protocol has been proven in [4] and the convergence speed of the Algorithm 1 is dependent on the initial mean value of the opinions, moreover the convergence speed has been demonstrated relatively stable even with the increase of n. Although the protocol is guaranteed to converge, however the convergence result is not always unique. The convergence result not only depends on the pair sequence, but also depends on the distribution pattern of the initial values, please see [4] for more details.…”
Section: Theoremmentioning
confidence: 96%
“…The convergence result not only depends on the pair sequence, but also depends on the distribution pattern of the initial values, please see [4] for more details.…”
Section: Theoremmentioning
confidence: 99%
“…In our earlier paper [4], we discussed a gossip-based consensus reaching protocol based on a uninorm aggregation operator. We analyze the convergence, the speed and the randomness of this protocol.…”
Section: Related Workmentioning
confidence: 99%
“…In our earlier work [4] , we described a gossiping protocol based on the aforementioned aggregator. Suppose we are given an initial vector A = (a 1 , a 2 , · · · , a n ), where a i ∈ [0, 1], i ∈ {1, · · · , n}.…”
Gossip-based protocols for group communication have attractive scalability and reliability properties. In this paper, we extends our previous work [4]: a gossip-based protocol that enable agents to reach a consensus based on a special uninorm aggregation operator. We theoretically analyze the convergence and the randomness features of the extensions. The extended models can be more flexible in demonstrating the uncertainty and the convergence characteristics of collective decision dynamics.
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