DISTRIBUTED SENSING, CONTROL AND DECISION MAKING IN MULTIAGENT AUTONOMOUS SYSTEMS

自主智能系统将在我们未来的社会中找到重要的应用。最初的应用将在以下领域：减灾领域的监测、情报收集和业务控制（地震、核灾难、军事战斗、海上漏油、运输基础设施故障、分析和协助恐怖袭击）、偏远地区的空间探索（特洛伊小行星、火星和行星周围的轨道观测、深海水下探索和近海石油勘探灾难的机器人技术），其次是大规模应用，如农业、搜索和救援，制造和自主家用机器人。这些自主系统将需要快速，适当，并在同一时间的信息合作伙伴，行动的机器人团队。它们也可以是具有感测和控制能力的基于计算网络的智能代理。这将是一个社会的要求，这些（半）自主操作的系统，以告知他们的人类监督员关于他们的行动背后的推理和他们的未来计划，在简明扼要的说明，为他们的安全性和社会的可接受性。基于网络的软件代理已经在我们的社会使用了一段时间。我们的社会正在经历信息交换革命，正在向网络智能设备发展。这些基础设施系统中的许多基于来自人类操作员或来自低维传感器测量的明确定义的离散输入和输出。然而，在高度复杂、不断变化的环境中，机器人智能的自主性几乎没有取得进展，需要快速感知、推理和采取行动。DARPA的Robocup项目报告了部分结果，这些项目没有提供全面的系统方法或没有完全公开。仅在机器人的基础设施环境中取得了进展。我们还没有一套自动驾驶汽车或代理系统在复杂的无基础设施环境中可靠和（半）自动运行的方法，以最少的人为监督有效地解决问题。原因是目前的智能代理技术没有提供解决方案。针对低功率和计算资源开发的具有简单计算节点的传感器网络没有提供解决方案。他们错过了高复杂性的概念抽象的能力板载一个单一的代理。由于典型的实时和通信瓶颈，这些类型的代理的计算不能被低复杂度代理的数据融合所取代。多智能体分散决策理论的方法已经开发出来，并非常成功地使用之前，这个项目，但还没有适当地利用多个复杂的agents.This项目旨在开发一种新的方法，自主合作的多智能体系统，这是提高我们的合作伙伴公司和机器人行业的技术能力一般。该项目将提供有关世界建模，态势感知，学习和信息管理船上一个单一的代理抽象的缺失功能。这些功能将使多代理合作中的高效实时决策和结构不良或无基础设施环境中的分散决策成为可能。这些方法将把数字计算能力与人类的概念结构联系起来，使机器人能够像人类一样用高层次和低层次的概念来模拟世界。

Autonomous intelligent systems will find important applications in our future society. Initial applications will be in the following areas: surveillance, intelligence gathering and operational control in the areas of disaster mitigation (earthquake, nuclear catastrophe, military combat, oil-spills at sea, transport infrastructure breakdown, analysis and assistance with terrorist attacks), space exploration at remote locations (at Trojan asteroids, on Mars and in orbit observations around planets, deep underwater explorations and robotics for offshore oil exploration disasters) followed by large scale applications such as agricultural, search and rescue, manufacturing, and autonomous household robots. These autonomous system will require quick, appropriate, and at the same time informative-to-partners, actions by teams of robots. They can also be computing network based intelligent agents with sensing and control capabilities. It will be a societal requirement that these (semi-)autonomously operating systems to inform their human supervisors about the reasoning behind their actions and their future plans in concise notes for their safety and acceptability by society.Network based software agents have been in use by our society for some time. Our society is going through information exchange revolution that is developing towards networked intelligent devices. Many of these infrastructure systems are based on well defined discrete inputs and outputs either from human operators or from low dimensional sensor measurements. Little progress has however been made in robot intelligence of autonomy where high complexity, changing environment is to be sensed, reasoned about and acted upon quickly. Partial results have been reported in DARPA, Robocup projects that do not provide comprehensive systematic approach or are not fully publicly available. Progress has only been made in heavily infrastructured environments of robots. We do not yet have the methodology for a set of autonomous vehicles or agent systems to operate reliably and (semi-)autonomously in complex infrastructure-free environments to solve problems efficiently with minimal human supervision. The reason is that current intelligent agent technology does not provide solutions. Sensor networks with simple computational nodes, that were developed for low power and computational resources do not provide solutions. They miss the ability of high complexity conceptual abstractions onboard a single agent. The computations of these type of agents cannot be substituted by data fusion of low complexity agents due to typical real-time and communication bottlenecks. Methods of multi-agent decentralized decision theory have been developed and very successfully used prior to this project but have not been properly exploited for multiple complex agents.This project intends to develop a new methodology for autonomous cooperating multi-agent systems that is to boost the technological capabilities of our partner companies and the robotics industry in general. The project will provide the missing capabilities of abstractions concerning world modeling, situational awareness, learning and information management onboard a single agent. These capabilities will enable efficient realtime decision making within multi-agent cooperation and decentralized decision making in poorly structured or infrastructure free environments. These methods will connect digital computing power with human conceptual structures to enable robots to model the world with layers of high and low level concepts as humans do.