Multi-agent Soft Constraint Aggregation - A Sequential Approach

We consider a scenario where several agents express their preferences over a common set of variable assignments, by means of a soft constraint problem for each agent, and we propose a procedure to compute a variable assignment which satisfies the agents’ preferences at best. Such a procedure considers one variable at a time and, at each step, asks all agents to express its preferences over the domain of that variable. Based on such preferences, a voting rule is used to decide on which value is the best for that variable. At the end, the values chosen constitute the returned variable assignment. We study several properties of this procedure and we show that the use of soft constraints allows for a great flexibility on the preferences of the agents, compared to similar work in setting where agents model their preferences via CP-nets, where several restrictions on the agents’ preferences need to be imposed to obtain