Conflict Resolution and Optimization

第一阶段的主要目标是开发用于重新配置和重新分配的基本算法方法，并通过与其他更实用的项目进行实际合作，确保这些方法是有用的，并适合中央协调委员会的总体目标。这是通过考虑在不同的信息和优化尺度下工作的方法(例如，通过数学编程的精确全局计算、通过用于逐步改进的局部方法的启发式方法)、不同的时间尺度(例如，在离线环境中应用的更耗时的方法、用于在线场景的更快的方法，但也细分为预处理/查询)以及不同的计算尺度(例如，集中式计算与博弈论机制设计)来实现的。这些规模往往是相互关联的(例如，分散计算可能与有限的信息相关)，而不是方法上的等价性(例如，即使在集中的情况下，也会在有限的信息下进行优化，从而产生在线算法领域)。算法领域的一般方法是为不同尺度的场景开发单独的方法，即考虑所描述的尺度的一种组合，并开发在这些特定条件下运行良好的强大的方法。第二阶段的主要总体目标是克服这一限制，研究复杂场景的多尺度、集成和交织方法，其中这些不同的尺度(信息和优化、时间、计算)不是分开处理的，而是以交换和并行的方式使用。这需要将单独的算法方法混合和统一到多尺度方法中，在B1中进行，与分布式合同和算法设计的概念方法和技术密切互动，并与其他项目合作，为复杂、结构化的情况使用，以及在与应用项目的合作伙伴进行案例研究的情况下，将整体方法与其他项目相结合。最值得注意的是，在组织一个机器人或车辆内的资源分配以及协调几个平台时，这些涉及研究机器人平台和自动驾驶车辆，这两者都构成了相关的科学挑战。除了我们的算法专业知识，我们在这两个应用领域都有相当多的经验，使这些非常有希望的目标。

The main objectives of Phase 1 aim at developing fundamental algorithmic methods for reconfiguration and reallocation, as well as ensuring that they are useful and suitable for the overall objectives of CCC, by engaging in practical collaboration with the other, more applied projects. This is achieved by considering methods that work at different information and optimization scales (e.g., exact global computation by mathematical programming, heuristic approaches by local methods for gradual improvement), different time scales (e.g., more time-consuming methods applied in an offline setting, faster methods for online scenarios, but also a subdivision into preprocessing/query), as well as different computation scales (e.g., centralized computation vs. game-theoretic mechanism design). These scales are often linked (for example, decentralized computation may correlate with limited information), without being methodologically equivalent (e.g., optimization under limited information arises even in centralized scenarios, giving rise to the area of online algorithms). A general approach in the field of algorithmics is to develop separate methods for scenarios at different scales, i.e., to consider one combination of the described scales, and develop powerful methods that work well under these specific conditions.The main overall objective for Phase 2 is to overcome this limitation and study of multi-scale, integrated and interleaving methods for complex scenarios, in which these different scales (information and optimization, time, computation) are not treated separately, but are employed in an interchanging and parallel fashion. This requires blending and unifying the separate algorithmic methods into multi-scale methods, to be carried out within B1, a close interaction with the conceptual approaches and techniques of distributed contract and algorithm design and employment for complex, structured scenarios in collaboration with other projects, and an integration of the overall methods with the other projects in the context of case studies with the partners from the applied projects. Most notably, these involve studying robot platforms and autonomous vehicles, both of which pose relevant scientific challenges, when organizing resource allocation within one robot or vehicle and when coordinating several platforms. Beyond our algorithmic expertise, we have considerable experience in both application areas, making these very promising targets.