We consider the population protocol model where indistinguishable state machines, referred to as agents, communicate in pairs. The communication graph specifies potential interactions (i.e., communication) between agent pairs. This paper addresses the complete graph identification problem, requiring agents to determine if their communication graph is a clique or not. We evaluate various settings based on: (i) the fairness preserved by the adversarial scheduler—either global fairness or weak fairness, and (ii) the knowledge provided to agents beforehand—either the exact population size n, a common upper bound P on n, or no prior information. Positively, we show that \(O(n^2)\) states per agent suffice to solve the complete graph identification problem under global fairness without prior knowledge. With prior knowledge of n, agents can solve the problem using only O(n) states under weak fairness.

错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

Complete Graph Identification in Population Protocols

  • Haruki Kanaya,
  • Yuichi Sudo

摘要

We consider the population protocol model where indistinguishable state machines, referred to as agents, communicate in pairs. The communication graph specifies potential interactions (i.e., communication) between agent pairs. This paper addresses the complete graph identification problem, requiring agents to determine if their communication graph is a clique or not. We evaluate various settings based on: (i) the fairness preserved by the adversarial scheduler—either global fairness or weak fairness, and (ii) the knowledge provided to agents beforehand—either the exact population size n, a common upper bound P on n, or no prior information. Positively, we show that \(O(n^2)\) states per agent suffice to solve the complete graph identification problem under global fairness without prior knowledge. With prior knowledge of n, agents can solve the problem using only O(n) states under weak fairness.