Analyzing the efficiency of strategies for MAS-based sensor interpretation and diagnosis

One of the factors holding back the application of multi-agent, distributed approaches to large-scale sensor interpretation and diagnosis problems is the lack of good techniques for predicting the performance of potential systems. In this paper we use a consideration of Bayesian network inference algorithms to construct formulas that describe the computational and communication resources required by several strategies for MAS-based distributed SI/diagnosis.