An autonomy-oriented strategy for distributed collaborative search problems in dynamic trust networks

A distributed collaborative search problem refers to find more than one partners to work together for accomplishing several tasks. So far, various strategies have been developed to solve such problems, whereas it is often required that all entities (e.g., people) act with blindness (e.g., random strategies) or a certain fixed rule (e.g., flooding strategies and degree-based local strategies). However, in the real-world, entities always act differently, due to the differences of their states, e.g., their abilities. Moreover, they are usually not willing to help others without profits, i.e., they are self-interested and profit-driven. Based on those considerations, this paper proposes a new autonomy-oriented strategy. Its core is that entities can determine to activate different search behaviors, according to their different states, received requests and corresponding profits, etc. The experimental results have shown the efficiency and robustness of this strategy in solving above-mentioned search problems in dynamic trust networks.