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Distributed CSPs by graph partitioning
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DOI：10.1016/j.amc.2006.05.090.png)
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Abstract
En 中文
Nowadays, many real problems in artificial intelligence can be modelled as constraint satisfaction problems (CSPs). A general CSP is known to be NP-complete. Nevertheless, distributed models may reduce the exponential complexity by partitioning the problem into a set of subproblems. In this paper, we present a preprocess technique to break a single large problem into a set of smaller loosely connected ones. These semi-independent CSPs can be efficiently solved and, furthermore, they can be solved concurrently. (c) 2006 Elsevier Inc. All rights reserved.
Keywords:
constraint satisfaction problems
distributed CSPs
artificial intelligence
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