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

One of the factors holding back the application of multiagent, 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. Categories and Subject Descriptors I.2.11 [ARTIFICIAL INTELLIGENCE]: Distributed Artificial Intelligence--Coherence and coordination General Terms Algorithms, Design, Performance Keywords computational complexity and agent systems; coordination of multiple agents/activities; distributed problem solving; distributed interpretation and diagnosis; Bayesian networks

• Type: Conference
• Year: 2003
• Where: ATAL
• Authors: Norman Carver, Ruj Akavipat
• Keywords: ATAL 2003, Bayesian Networks, Intelligent Agents, Large-scale Sensor Interpretation, Mas-based Distributed Si/diagnosis