Publication Type

Conference Proceeding Article

Publication Date

2008

Abstract

In distributed constraint optimization problems, dynamic programming methods have been recently proposed (e.g. DPOP). In dynamic programming many valuations are grouped together in fewer messages, which produce much less networking overhead than search. Nevertheless, these messages are exponential in size. The basic DPOP always communicates all possible assignments, even when some of them may be inconsistent due to hard constraints. Many real problems contain hard constraints that significantly reduce the space of feasible assignments. This paper introduces H-DPOP, a hybrid algorithm that is based on DPOP, which uses Constraint Decision Diagrams (CDD) to rule out infeasible assignments, and thus compactly represent UTIL messages. Experimental results show that H-DPOP requires several orders of magnitude less memory than DPOP, especially for dense and tightly-constrained problems.

Discipline

Artificial Intelligence and Robotics

Research Areas

Intelligent Systems and Decision Analytics

Publication

AAAI Conference on Artificial Intelligence (AAAI)

Volume

1

First Page

325

Last Page

330

ISBN

9781577353683

Share

COinS