Distributed constraint optimization with MULBS: A case study on collaborative meeting scheduling |
| |
Authors: | Fabrí cio Enembreck,Jean-Paul André Barthè s |
| |
Affiliation: | a Pontifícia Universidade Católica do Paraná, Graduate Program on Informatics—PPGIA, av. Imaculada Conceição, 1155, Curitiba—Paraná, Brazil b Université de Technologie de Compiègne, Centre de Recherches Royallieu, Compiègne, France |
| |
Abstract: | This paper introduces MULBS, a new DCOP (distributed constraint optimization problem) algorithm and also presents a DCOP formulation for scheduling of distributed meetings in collaborative environments. Scheduling in CSCWD can be seen as a DCOP where variables represent time slots and values are resources of a production system (machines, raw-materials, hardware components, etc.) or management system (meetings, project tasks, human resources, money, etc). Therefore, a DCOP algorithm must find a set of variable assignments that maximize an objective function taking constraints into account. However, it is well known that such problems are NP-complete and that more research must be done to obtain feasible and reliable computational approaches. Thus, DCOP emerges as a very promising technique: the search space is decomposed into smaller spaces and agents solve local problems, collaborating in order to achieve a global solution. We show with empirical experiments that MULBS outperforms some of the state-of-the-art algorithms for DCOP, guaranteeing high quality solutions using less computational resources for the distributed meeting scheduling task. |
| |
Keywords: | DCOP Distributed artificial intelligence Distributed meeting scheduling |
本文献已被 ScienceDirect 等数据库收录! |
|