Abstract
Partial Cooperation is a paradigm and a corresponding
model, proposed to represent multi-agent
systems in which agents are willing to cooperate
to achieve a global goal, as long as some minimal
threshold on their personal utility is satisfied. Distributed
local search algorithms were proposed in
order to solve asymmetric distributed constraint optimization
problems (ADCOPs) in which agents are
partially cooperative.
We contribute by: 1) extending the partial cooperative
model to allow it to represent dynamic cooperation
intentions, affected by changes in agents’
wealth, in accordance with social studies literature.
2) proposing a novel local search algorithm
in which agents receive indications of others’ preferences
on their actions and thus, can perform actions
that are socially beneficial. Our empirical
study reveals the advantage of the proposed algorithm
in multiple benchmarks. Specifically, on realistic
meeting scheduling problems it overcomes
limitations of standard local search algorithms.
Keywords
- Agent-based and Multi-agent Systems: Coordination and Cooperation
- Constraints and SAT: Distributed Constraints
