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Solving distributed constraint optimization problems using logic programming*†

Published online by Cambridge University Press:  27 June 2017

TIEP LE ,
TRAN CAO SON ,
ENRICO PONTELLI  and
WILLIAM YEOH
TIEP LE
Affiliation:
Computer Science Department, New Mexico State University, Las Cruces, NM 88001, USA (e-mails: tile@cs.nmsu.edu, tson@cs.nmsu.edu, epontell@cs.nmsu.edu, wyeoh@cs.nmsu.edu)
TRAN CAO SON
Affiliation:
Computer Science Department, New Mexico State University, Las Cruces, NM 88001, USA (e-mails: tile@cs.nmsu.edu, tson@cs.nmsu.edu, epontell@cs.nmsu.edu, wyeoh@cs.nmsu.edu)
ENRICO PONTELLI
Affiliation:
Computer Science Department, New Mexico State University, Las Cruces, NM 88001, USA (e-mails: tile@cs.nmsu.edu, tson@cs.nmsu.edu, epontell@cs.nmsu.edu, wyeoh@cs.nmsu.edu)
WILLIAM YEOH
Affiliation:
Computer Science Department, New Mexico State University, Las Cruces, NM 88001, USA (e-mails: tile@cs.nmsu.edu, tson@cs.nmsu.edu, epontell@cs.nmsu.edu, wyeoh@cs.nmsu.edu)
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Abstract

This paper explores the use of Answer Set Programming (ASP) in solving Distributed Constraint Optimization Problems (DCOPs). The paper provides the following novel contributions: (1) it shows how one can formulate DCOPs as logic programs; (2) it introduces ASP-DPOP, the first DCOP algorithm that is based on logic programming; (3) it experimentally shows that ASP-DPOP can be up to two orders of magnitude faster than DPOP (its imperative programming counterpart) as well as solve some problems that DPOP fails to solve, due to memory limitations; and (4) it demonstrates the applicability of ASP in a wide array of multi-agent problems currently modeled as DCOPs.

Keywords

DCOPDPOPlogic programmingASP
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 17 , Issue 4 , July 2017 , pp. 634 - 683
Copyright
Copyright © Cambridge University Press 2017 

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Footnotes

*

This article extends our previous conference paper (Le et al. 2015) in the following manner: (1) it provides a more thorough description of the ASP-DPOP algorithm; (2) it elaborates on the algorithm's theoretical properties with complete proofs; and (3) it includes additional experimental results.

†

This research is partially supported by NSF grants HRD-1345232 and DGE-0947465.

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