Planning and execution of actions for agents in dynamic environments

在人工智能中，代理是一种计算机程序，能够获取有关其环境的信息，以便自主执行有目的的任务。它可以是一个控制自主机器人的软件，使用传感器感知环境，做出推理来解释感知，以及计划和执行行动以实现目标。它也可以是实时策略(RTS)游戏中的“人工智能”或“AI”控制单元，观察玩家的动作以决定如何做出反应，以提供一个有趣的挑战。赋予这样一个代理自主计划和执行操作的能力仍然是一个巨大的挑战。这样的计划和计划执行能力应该与代理人的感知和行动能力相协调，同时考虑到其状态、环境的状态和要实现的目标。如今，最有效的规划算法使用表达能力有限的计划表示法，以及面向确定性环境的启发式和搜索控制方法。当这些算法用于动态环境中时，如RTS游戏或移动机器人，所产生的规划能力非常有限。此外，目前协调此类代理的计划和反应行动的方法效果有限。为了解决这些问题，该项目将开发新的规划算法，以及协调计划行为和反应行为执行的新方法。

In artificial intelligence, an agent is a computer program capable of acquiring information about its environment in order to perform purposeful tasks autonomously. It could be a piece of software controlling an autonomous robot, using sensors to perceive the environment, making inferences to interpret the percepts, as well as planning and executing actions to achieve goals. It could also be the 'Artificial Intelligence' or 'AI' controlling units in a Real-Time Strategy (RTS) game, observing the moves made by a player to decide how to react in order to offer an entertaining challenge. Endowing such an agent with the capability to autonomously plan and execute actions is is still a tremendous challenge. Such a planning and plan execution capability should be coordinated with the agent's capabilities to perceive and act, taking into account its state, that of the environment, and the goal to be achieved. Today most efficient planning algorithms use plan representations with limited expressiveness, as well as heuristics and search-control methods geared towards deterministic environments. When these algorithms are used in dynamic environments such as RTS games or mobile robots, the resulting planning capability is very limited. Furthemore, current methods for coordinating planned and reactive actions for such agents have a limited effectiveness. To address these problems, this project will develop new planning algorithms, as well as new methods for coordinating the execution of planned and reactive behaviours.