A fast graphic-based information valuation algorithm for cooperative information sharing
Haixiao Hu, Xu Yang, Yulin Zhang, Ming Liu · 2017
Information sharing is critical to multi-agent team for cooperative decision making in dynamic and partially observable environments. Other than building a full information coverage, if agents in a team can be self-directed to valuate a potential receiver and where to communicate, the coordination efficiency will be greatly enhanced. Although intensive studies of information valuation approaches have been developed in ontology graph matching and natural language processing, these models fail to perform fast reasoning for large-scale decentralized agents dynamic coordination. In this paper, we propose a fast information valuation approach based on a complex network graph model, which helps to indicate the information importance. Similar to vague information valuation by human, the key is that important information always significantly changes their complex information graph with its incorporation. Therefore, we calculate the semantic based value of this new information in a graph model and build a local graph evaluation algorithm to estimate information graph evolution, instead of performing expensive complete graph search. The advantage is that the local valuation algorithm can be easily transformed into efficient queries in agents' information base so that they can make fast decisions. Although the decision may not be precise, similar to human communication, the information sharing performance is good enough to disseminate valuable information in the multi-agent team. We demonstrate the feasibility of the proposed information valuation approach in a multi-agent cooperation case study.