RI: Small: Variation and self-organization in multi-agent systems

该项目旨在了解如何使用代理间的变化，以提高基于群的系统解决分散任务分配问题的能力。 基于群的系统由大量独立的代理组成，这些代理共同行动以实现超出单个代理范围的目标。 这种系统可以应用于放牧、森林火灾控制、人群控制、周边保护和危险废物清理等问题，这些问题由多项任务组成，其需求可能会随着时间的推移而变化。 群中的代理可以是物理机器人或虚拟软件代理。 因为这些系统是分散的，没有中央控制器，每个代理独立决定什么任务和什么时候承担。 有效和高效的分配和任务之间的代理重新分配（任务需求随时间变化）是至关重要的良好的群体性能。 除了在任何给定的时间在每个任务上保持适当数量的代理之外，群还必须避免或最小化诸如太多或太少代理响应的极端响应、代理撤销和重做彼此的工作时浪费的能量以及可能完全阻止总体目标实现的死锁等问题。 社会昆虫社会的研究表明，代理人之间的变化是一个必要的因素，有效和高效的分工在生物群。 从生物学中获得灵感，这项工作研究了智能体间的变化如何影响计算群中的分散任务分配问题，以及什么类型的变化最有效地产生稳定，鲁棒和适应性强的群体。 首先，系统研究了智能体间变异与群体自组织行为之间的关系，建立了智能体间变异与群体自组织行为之间的关系模型。 这一阶段将研究代理决策特征的不同类型的变化如何影响系统级的稳定性和适应性。 其次，研究动态演化系统变化的方法和权衡。 本阶段将研究如何使用机器学习方法（如进化计算）的原理来动态学习和调整代理间的变化，以响应不断变化的任务需求。 第三，将第一阶段和第二阶段的结论应用于多智能体羊群问题并进行检验。 多智能体羊群问题是一系列需要劳动分工的问题的代表，涉及搜索，收集，遏制和协调运动。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

This project seeks to understand how to use inter-agent variation to improve the ability of swarm-based systems to solve the decentralized task allocation problem. Swarm-based systems consist of large numbers of independent agents that act collectively to accomplish goals beyond the scope of a single agent. Such systems may be applied to problems such as herding, forest fire containment, crowd control, perimeter protection, and hazardous waste clean up, which consist of multiple tasks with demands that may vary over time. The agents in a swarm may be physical robots or virtual software agents. Because these systems are decentralized and have no central controller, each agent decides independently what task to take on and when. Effective and efficient allocation and reallocation of agents among tasks (as task demands change over time) is crucial to good swarm performance. In addition to maintaining an appropriate number of agents on each task at any given time, swarms must also avoid or minimize problems such as extreme responses in which too many or too few agents respond, wasted energy when agents undo and redo each others' work, and deadlocks which may prevent accomplishment of the overall goal altogether. Studies on social insect societies indicate that inter-agent variation is a necessary element for effective and efficient division of labor in biological swarms. Taking inspiration from biology, this work investigates how inter-agent variation affects the decentralized task allocation problem in computational swarms and what types of variation are most effective in producing stable, robust, and adaptable swarms.This project consists of three phases. First, carry out a systematic study to build a model of the relationship between inter-agent variation and the self-organizing behavior of swarms. This phase will examine how different types of variation in agent decision making characteristics affect system level stability and adaptability. Second, investigate methods for and trade offs of dynamically evolving system variation. This phase will investigate how principles from machine learning methods such as evolutionary computation may be used to dynamically learn and adjust inter-agent variation in response to changing task demands. Third, apply and test conclusions from phase one and two to a multi-agent herding problem. The multi-agent herding problem is representative of a range of problems that require division of labor involving search, gathering, containment, and coordinated movement.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.