动态环境中自主无人机的ASP体系结构：进展报告
An ASP-Based Architecture for Autonomous UAVs in Dynamic Environments: Progress Report
摘要：
传统的人工智能推理技术已经成功地应用于许多领域，包括物流，调度和游戏。本文是一个项目的一部分，旨在研究如何将这种技术扩展到协调团队的无人机（UAV）在动态环境中。特别具有挑战性的是真实世界的环境，无人机和其他网络设备必须进行通信以协调-通信行为既不可靠也不自由。这种以网络为中心的环境在军事、公共安全和商业应用中很常见，但大多数研究（甚至是多智能体规划）通常将分布式智能体之间的通信视为给定。我们通过开发一个基于回答集编程（ASP）的代理体系结构和推理算法来解决这一挑战。ASP已被选为这项任务，因为它使高灵活性的表示，知识和推理任务。虽然ASP已成功地用于一些应用程序，基于ASP的架构已经研究了大约十年，据我们所知，这是第一个实际应用的一个完整的基于ASP的代理架构。它也是ASP的第一个实际应用，涉及集中推理，分散推理，执行监控和网络通信推理的组合。这项工作已被经验验证，使用分布式网络为中心的软件评估测试平台和结果提供指导，设计人员如何理解和控制智能系统，在这些环境中运行。
英文摘要：
Traditional AI reasoning techniques have been used successfully in many domains, including logistics, scheduling and game playing. This paper is part of a project aimed at investigating how such techniques can be extended to coordinate teams of unmanned aerial vehicles (UAVs) in dynamic environments. Specifically challenging are real-world environments where UAVs and other network-enabled devices must communicate to coordinate---and communication actions are neither reliable nor free. Such network-centric environments are common in military, public safety and commercial applications, yet most research (even multi-agent planning) usually takes communications among distributed agents as a given. We address this challenge by developing an agent architecture and reasoning algorithms based on Answer Set Programming (ASP). ASP has been chosen for this task because it enables high flexibility of representation, both of knowledge and of reasoning tasks. Although ASP has been used successfully in a number of applications, and ASP-based architectures have been studied for about a decade, to the best of our knowledge this is the first practical application of a complete ASP-based agent architecture. It is also the first practical application of ASP involving a combination of centralized reasoning, decentralized reasoning, execution monitoring, and reasoning about network communications. This work has been empirically validated using a distributed network-centric software evaluation testbed and the results provide guidance to designers in how to understand and control intelligent systems that operate in these environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
