Learning Optimal Temperature Region for Solving Mixed Integer Functional DCOPs
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Main track. Pages 268-275. https://doi.org/10.24963/ijcai.2020/38
Distributed Constraint Optimization Problems (DCOPs) are an important framework for modeling coordinated decision-making problems in multi-agent systems with a set of discrete variables. Later works have extended DCOPs to model problems with a set of continuous variables, named Functional DCOPs (F-DCOPs). In this paper, we combine both of these frameworks into the Mixed Integer Functional DCOP (MIF-DCOP) framework that can deal with problems regardless of their variables' type. We then propose a novel algorithm - Distributed Parallel Simulated Annealing (DPSA), where agents cooperatively learn the optimal parameter configuration for the algorithm while also solving the given problem using the learned knowledge. Finally, we empirically evaluate our approach in DCOP, F-DCOP, and MIF-DCOP settings and show that DPSA produces solutions of significantly better quality than the state-of-the-art non-exact algorithms in their corresponding settings.
Agent-based and Multi-agent Systems: Coordination and Cooperation
Constraints and SAT: Constraint Optimization
Constraints and SAT: Distributed Constraints
Agent-based and Multi-agent Systems: Multi-agent Learning