CAREER: Planning, Execution, and Learning in Autonomous Agents

9502548维罗索，曼努埃拉·卡内基-梅隆大学，年薪45,000-12,000美元职业生涯：自主机器人代理的规划、执行和学习自主机器人代理需要计划和实现高级任务的能力。现有的智能体可以实现对象回避等底层执行目标，但缺乏推理和执行多个交互任务的能力。由机器人集成规划和执行是一项复杂的任务，需要从先前的经验中学习，以显著提高智能代理的整体能力。我们将探索计划、执行和学习的集成，将机器人的成功和失败合并到知识库中，以提高未来的目标实现性能。该项目需要调查各种系统和知识工程问题，包括：规划者和执行者之间的沟通；机器人代理人、规划系统和随时可能要求执行某些任务的人类用户之间的互动；以及收集和解释信息以从经验中学习。这项研究的科学目标是理解如何利用过去的经验来解决受多种因素影响的复杂情况下的问题，这些因素具有不同程度的可控性和相互依存性。这一目标的部分实现对机器人学和认知科学或人工智能研究社区都具有重要意义，可以弥合高级规划者-学习者和在现实世界中执行任务的自主智能体之间的表示和推理差距。

IRI-9502548 Veloso, Manuela Carnegie-Mellon University $45,000 - 12mos CAREER: Planning, Execution, and Learning in Autonomous Robotic Agents Autonomous robotic agents need the ability to plan for and achieve high-level tasks. Current agents can achieve low-level execution goals such as object avoidance, but lack the ability to reason about and to carry out multiple interacting tasks. Integrating planning and execution by a robot is a complex task, requiring learning from prior experience to improve significantly the overall capabilities of the intelligent agent. We will explore the integration of planning, execution, and learning, where the robot's successes and failures are incorporated into a knowledge base to improve future goal-achievement performance. This project entails the investigation of a variety of system and knowledge engineering issues, including: communication between the planner and the executor; interaction between the robotic agent, the planning system, and the human user who may, at any time, request that certain tasks be performed; and gathering and interpretation of information to learn from experience. The scientific goal underlying this research is to understand the use of past experience to solve problems in complex situations affected by multiple factors with different degrees of controllability and interdependency. Even partial achievement of this goal could be of great significance to both the robotics and cognitive science or artificial intelligence research communities, bridging the representation and reasoning gap between high level planner-learner and autonomous agent executing tasks in the real world