Global/Local Dynamic Models [electronic resource].
- Washington, D.C. : United States. Dept. of Energy, 2006. and Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy.
- Physical Description:
- PDF-file: 8 pages; size: 0.1 Mbytes
- Additional Creators:
- Lawrence Berkeley National Laboratory, United States. Department of Energy, and United States. Department of Energy. Office of Scientific and Technical Information
- Restrictions on Access:
- Free-to-read Unrestricted online access
- Many dynamic systems involve a number of entities that are largely independent of each other but interact with each other via a subset of state variables. We present global/local dynamic models (GLDMs) to capture these kinds of systems. In a GLDM, the state of an entity is decomposed into a globally influenced state that depends on other entities, and a locally influenced state that depends only on the entity itself. We present an inference algorithm for GLDMs called global/local particle filtering, that introduces the principle of reasoning globally about global dynamics and locally about local dynamics. We have applied GLDMs to an asymmetric urban warfare environment, in which enemy units form teams to attack important targets, and the task is to detect such teams as they form. Experimental results for this application show that global/local particle filtering outperforms ordinary particle filtering and factored particle filtering.
- Published through SciTech Connect., 10/10/2006., "ucrl-conf-225258", Presented at: International Joint Conference on Artificial Intelligence, Hyderabad, India, Jan 06 - Jan 12, 2007., and Das, S; Lawless, D; Pfeffer, A; Ng, B.
- Funding Information:
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