Weighted Opinion Sharing Model for Cutting Link and Changing Information among Agents as Dynamic Environment

摘要

This paper proposes a weighted opinion-sharing method called conformity-autonomous adaptive tuning (C-AAT) that enables agents to communicate and share correct information in a small-world network even when the links and information change dynamically. Concretely, each agent estimates weights for each of its neighbors by comparing their opinions with its own, increasing the weight if both are the same and decreasing it otherwise. To investigate the proposed method's effectiveness, experiments were conducted for three scenarios: (1) a static network with sensor agents that were almost equally likely to share incorrect environment information; (2) a static network with sensor agents whose probability of sharing incorrect information changed over time; (3) a dynamic network where some agent links were randomly cut over time. The experimental results led to three conclusions about C-AAT: (i) it can make the agents' opinions robust against incorrect sensor agent opinions by decreasing the weights; (ii) it can decrease the weights of agents conveying incorrect opinions with varying probabilities to prevent incorrect opinions being shared; and (iii) it can help agents share correct opinions by increasing the weights of their neighbors even if the agents receive fewer opinions due to links being cut.