In cooperative Multi-Agent Planning (MAP), a set of goals has to be achieved by a set of agents. Independently of whether they perform a pre-assignment of goals to agents or they directly search for a solution without any goal assignment, most previous works did not focus on a fair distribution/achievement of goals by agents. This paper adapts well-known fairness schemes to MAP, and introduces two novel approaches to generate cost-aware fair plans. The first one solves an optimization problem to pre-assign goals to agents, and then solves a centralized MAP task using that assignment. The second one consists of a planning-based compilation that allows solving the joint problem of goal assignment and planning while taking into account the given fairness scheme. Empirical results in several standard MAP benchmarks show that these approaches outperform different baselines. They also show that there is no need to sacrifice much plan cost to generate fair plans.

翻译：在协作式多智能体规划（MAP）中，一组目标需要由一组智能体共同完成。无论这些智能体是预先对目标进行分配，还是直接搜索无目标分配的解决方案，以往大多数研究都未关注智能体间目标的公平分配/实现。本文将经典公平性方案适配至MAP领域，并提出两种新型方法来生成成本感知的公平规划。第一种方法通过求解优化问题预先将目标分配给智能体，再基于该分配解决集中式MAP任务；第二种方法采用基于规划的编译技术，在考虑给定公平性方案的前提下，联合求解目标分配与规划问题。在多个标准MAP基准测试中的实验结果表明，这些方法优于不同基线方案，同时表明无需牺牲过多规划成本即可生成公平规划。