Multi-Robot and Multi-Agent Systems demonstrate collective (swarm) intelligence through systematic and distributed integration of local behaviors in a group. Agents sharing knowledge about the mission and environment can enhance performance at individual and mission levels. However, this is difficult to achieve, partly due to the lack of a generic framework for transferring part of the known knowledge (behaviors) between agents. This paper presents a new knowledge representation framework and a transfer strategy called KT-BT: Knowledge Transfer through Behavior Trees. The KT-BT framework follows a query-response-update mechanism through an online Behavior Tree framework, where agents broadcast queries for unknown conditions and respond with appropriate knowledge using a condition-action-control sub-flow. We embed a novel grammar structure called stringBT that encodes knowledge, enabling behavior sharing. We theoretically investigate the properties of the KT-BT framework in achieving homogeneity of high knowledge across the entire group compared to a heterogeneous system without the capability of sharing their knowledge. We extensively verify our framework in a simulated multi-robot search and rescue problem. The results show successful knowledge transfers and improved group performance in various scenarios. We further study the effects of opportunities and communication range on group performance, knowledge spread, and functional heterogeneity in a group of agents, presenting interesting insights.
翻译:多机器人和多行为者系统通过在团体中系统化和分散地整合地方行为,展示集体(温和)情报。关于任务和环境的知识分享机构可以提高个人和特派团一级的绩效。然而,这很难实现,部分原因是缺乏一个在代理之间转让部分已知知识(行为)的通用框架。本文介绍了一个新的知识代表框架和一个称为KT-BT的转让战略:通过行为树进行知识转让。KT-BT框架遵循一个通过在线行为树框架的查询-答复更新机制,在网上Behavior Tree框架中,代理对未知条件进行查询,利用条件-行动控制分流以适当知识回应。我们采用了一个叫做字符串BT的新式的语法结构,将知识编码起来,使行为共享成为可能。我们从理论上调查KT-BT框架在使整个群体实现高知识同质性,而与混合系统相比,没有分享其知识的能力。我们在一个模拟多机器人搜索和救援树框架中广泛核查我们的框架。我们通过一个模拟的多机器人搜索和救援效果小组,在功能分析中展示了成功的业绩转让和改进了小组。我们关于功能分析机会。