Evolution of Communication in Multi-Agent Systems

这个项目将探讨的方式，沟通和解决问题的自主代理，如移动的机器人团队的群体相互作用，并检查在这些群体中的任务特定的语言的方法。目标是提出新的算法和协议，利用进化算法的力量，将联合收割机问题解决和通信结合起来。更具体地说，PI将试图证明，在任务环境中，其约束条件可以建模为进化压力，一组自主代理可能会演变出一个特定于任务的通信系统和简单的语言，这将提高他们的整体效率。此外，在环境中，惩罚通信的努力，如监视任务或嘈杂的通信网络，一个特定于任务的通信系统，只包括那些语音行为发现完成任务所必需的将最大限度地减少代理间的通信，产生显着的组解决问题的改进。这个多方面的问题将通过将其框架在有据可查的捕食者/猎物域中来处理。在工程领域，掠食者被视为执行搜索和救援或分布式空间探索等任务的多机器人组的代表。这个环境将被扩展到包括具有不同的传感和驱动能力，必须有效地协调，以实现组目标的广义，异构机器人组的分析。在这种任务中，环境的确切性质可能事先并不知道。因此，将使用一个模拟工具，允许通信能力的发展，以最好地匹配代理的能力，一个未知的或部分已知的任务环境。模拟是模块化的，并允许包括或排除各种选择力量，以隔离的因素，使有效的沟通发展。 PI将开发一个硬件测试平台，可供许多学生用于独立的研究项目。这个试验台也将是一个极端的K-12推广工作的亮点。

This project will explore the way that communication and problem solving interact in groups of autonomous agents, such as mobile robot teams, and to examine methods for evolving task-specific language in these groups. The goal is to come up with new algorithms and protocols that exploit the power of evolutionary algorithms to combine problem solving and communication. More specifically, the PI will try to demonstrate that in task environments whose constraints can be modeled as evolutionary pressures, a group of autonomous agents may evolve a task-specific communication system and simple language that will increase their overall effectiveness. Moreover in environments that penalize communication efforts such as surveillance tasks or noisy communication networks, a task-specific communication system including only those speech acts found necessary to complete the task will minimize inter-agent communications, yielding significant group problem-solving improvement. This multi-faceted problem will be approached by framing it within the well-documented Predator/Prey domain. In the realm of engineering, the predators are seen as representative of a multi-robot group performing a task such as search and rescue or distributed space exploration. This environment will be extended to include the analysis of groups of generalized, heterogeneous robots with different sensing and actuation abilities, which must coordinate efficiently to achieve the group goal. The exact nature of the environment in such tasks may not be known a priori. Hence, a simulation tool will be used that allows communication abilities to evolve in order to best match the agent's abilities to an unknown or partially known task environment. The simulation is modular and allows for the inclusion or exclusion of various selection forces in order to isolate the factors that enable effective communication to develop. The PI will develop a hardware testbed that can be used by many students for independent research projects. This testbed will also be the highlight of an extreme K-12 outreach effort.