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

RI：小：多智能体系统中的变化和自组织

• 批准号: 1816777
• 负责人: Annie Wu
• 金额: $ 43.37万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1816777&HistoricalAwards=false
• 关键词: RI; Small; Variation; self; organization

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.

本项目旨在了解如何利用智能体间的变化来提高基于群体的系统解决分散任务分配问题的能力。基于群体的系统由大量独立的代理组成，这些代理集体行动以完成超出单个代理范围的目标。这样的系统可以应用于诸如放牧、森林防火、人群控制、周边保护和危险废物清理等问题，这些问题由多种任务组成，其需求可能随着时间的推移而变化。集群中的代理可以是物理机器人，也可以是虚拟软件代理。因为这些系统是分散的，没有中央控制器，每个代理独立地决定什么时候承担什么任务。在任务之间有效和高效地分配和重新分配代理（因为任务需求随时间变化）对于良好的群性能至关重要。除了在任何给定时间保持每个任务上的适当数量的代理之外，群体还必须避免或最小化诸如极端响应（太多或太少的代理响应），代理撤消和重做彼此的工作时浪费的能量，以及可能妨碍完成总体目标的死锁等问题。对昆虫社会的研究表明，昆虫间的变异是生物群体有效分工的必要因素。受生物学的启发，本研究探讨了计算群中agent间的变化如何影响分散的任务分配问题，以及哪种类型的变化在产生稳定、健壮和适应性强的群体中最有效。本项目分为三个阶段。首先，进行系统研究，建立agent间变异与群体自组织行为之间的关系模型。本阶段将研究不同类型的智能体决策特征变化如何影响系统级稳定性和适应性。其次，研究动态演化的系统变化的方法和权衡。这一阶段将研究如何使用机器学习方法的原理，如进化计算，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测试。多智能体羊群问题代表了一系列需要分工的问题，包括搜索、收集、遏制和协调移动。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Heterogeneous response intensity ranges and response probability improve goal achievement in multi-agent systems

异构响应强度范围和响应概率提高了多智能体系统中的目标实现

• 发表时间: 2020
• 期刊: Proceedings of the 12th International Conference on Swarm Intelligence
• 作者: Mathias, H. David;Wu, Annie S.;Ruetten, Laik
• 通讯作者: Ruetten, Laik

Dynamic Response Thresholds: Heterogeneous Ranges Allow Specialization While Mitigating Convergence to Sink States

• DOI: 10.1007/978-3-030-60376-2_9
• 发表时间: 2020
• 作者: A. Wu;H. Mathias

Pid-Inspired Modifications in Response Threshold Models In Swarm Intelligent Systems

• DOI: 10.1145/3583131.3590442
• 发表时间: 2023-04
• 期刊: Proceedings of the Genetic and Evolutionary Computation Conference
• 作者: Maryam Kebari;A. Wu;David Mathias

Response thresholds generalize across problem instances for a deterministic response multiagent system

响应阈值可概括确定性响应多代理系统的问题实例

• 发表时间: 2021
• 期刊: Proceedings of the Genetic and Evolutionary Computation Conference
• 作者: Mathias, H. David;Wu, Annie S.;and Dang, Daniel
• 通讯作者: and Dang, Daniel

Improving multi-agent system coordination via intensity variation

通过强度变化改善多智能体系统协调

• 发表时间: 2020
• 期刊: Proceedings of the 33rd International Florida Artificial Intelligence Research Society Conference
• 作者: Mathias, H. David;Wu, Annie S.;Ruetten, Laik;Coursin, Eric
• 通讯作者: Coursin, Eric