Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Structured representation of complex stochastic systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Qualitative analysis of distributed physical systems with applications to control synthesis
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Image Segmentation Using Local Variation
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
An {\it bf O(N)} Algorithm for Three-Dimensional N-body Simulations
An {\'it bf O(N)} Algorithm for Three-Dimensional N-body Simulations
Model decomposition and simulation: a component based qualitative simulation algorithm
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Influence-based model decomposition for reasoning about spatially distributed physical systems
Artificial Intelligence
``Seeing'' Objects in Spatial Datasets
IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
Relation-based aggregation: finding objects in large spatial datasets
Intelligent Data Analysis
Gaussian process models of spatial aggregation algorithms
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
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Recent rapid advances in MEMS and information processing technology have enabled a new generation of AI robotic systems -- so-called Smart Matter systems - that are sensor rich and physically embedded. These systems range from decentralized control systems that regulate building temperature (smart buildings) to vehicle on-board diagnostic and control systems that interrogate large amounts of sensor data. One of the core tasks in the construction and operation of these Smart Matter systems is to synthesize optimal control policies using data rich models for the systems and environment. Unfortunately, these models may contain thousands of coupled real-valued variables and are prohibitively expensive to reason about using traditional optimization techniques such as neural nets and genetic algorithms. This paper introduces a general mechanism for automatically decomposing a large model into smaller subparts so that these subparts can be separately optimized and then combined. The mechanism decomposes a model using an influence graph that records the coupling strengths among constituents of the model. This paper demonstrates the mechanism in an application of decentralized optimization for a temperature regulation problem. Performance data has shown that the approach is much more efficient than the standard discrete optimization algorithms and achieves comparable accuracy.