Multi-Context System for Optimization Problems
Abstract
This paper proposes Multi-context System for Optimization Problems (MCS-OP) by introducing conditional costassignment bridge rules to Multi-context Systems (MCS). This novel feature facilitates the definition of a preorder among equilibria, based on the total incurred cost of applied bridge rules. As an application of MCS-OP, the paper describes how MCS-OP can be used in modeling Distributed Constraint Optimization Problems (DCOP), a prominent class of distributed optimization problems that is frequently employed in multi-agent system (MAS) research. The paper shows, by means of an example, that MCS-OP is more expressive than DCOP, and hence, could potentially be useful in modeling distributed optimization problems which cannot be easily dealt with using DCOPs. It also contains a complexity analysis of MCS-OP.
Cite
Text
Le et al. "Multi-Context System for Optimization Problems." AAAI Conference on Artificial Intelligence, 2019. doi:10.1609/AAAI.V33I01.33012929Markdown
[Le et al. "Multi-Context System for Optimization Problems." AAAI Conference on Artificial Intelligence, 2019.](https://mlanthology.org/aaai/2019/le2019aaai-multi/) doi:10.1609/AAAI.V33I01.33012929BibTeX
@inproceedings{le2019aaai-multi,
title = {{Multi-Context System for Optimization Problems}},
author = {Le, Tiep and Son, Tran Cao and Pontelli, Enrico},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2019},
pages = {2929-2937},
doi = {10.1609/AAAI.V33I01.33012929},
url = {https://mlanthology.org/aaai/2019/le2019aaai-multi/}
}