Influence-Based Model Decomposition

Recent rapid advances in MEMS and information processing technologyhaveenableda newgeneration of AI robotic systems -- so-called SmartMatter systems -that are sensor rich and physically embedded. These systems range from decentralized control systems that regulate building temperature(smart buildings) to vehicle on-boarddiagnostic and control systemsthat interrogate large amounts of sensor data. Oneof the core tasks in the construction and operation of these SmartMatter systems is to synthesize optimal control policies using data rich modelsfor the systemsandenvironment. Unfortunately, these modelsmaycontain thousandsof coupledreal-valued variables and are prohibitively expensiveto reason about using traditional optimizationtechniques such as neural nets and genetic algorithms. This paper introduces a general mechanism for automatically decomposinga large model into smaller subparts so that these subparts can be separately optimized and then combined. The mechanism decomposesa model using an influence graph that records the coupling strengths among constituents of the model. This paper demonstrates the mechanismin an application of decentralizedoptimizationfor a temperature regulation problem. Performancedata has shownthat the approach is muchmoreefficient than the standard discrete optimization algorithms and achieves comparableaccuracy.

Authors

• Christopher Bailey-Kellogg
• Dartmouth College
• Feng Zhao University of Science and Technology of China
• Xerox Palo Alto Research Center

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