PLANGENT: An Approach to Making Mobile Agents Intelligent
PLANGENT: An Approach to Making Mobile Agents Intelligent
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PLANGEN：一种使移动代理智能化的方法
DOI：
10.1109/4236.612215
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发表时间：
1997
期刊：
影响因子：
--
通讯作者：
S. Honiden
中科院分区：
文献类型：
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作者：
Akihiko Ohsuga;Y. Nagai;Yutaka Irie;Masanori Hattori;S. Honiden
网络环境为计算机用户提供了使用分布式信息和服务来完成任务的选项。但是，收集信息和使用网络中分发的服务需要确切了解任务需要哪种信息和服务，它们在哪里以及如何获得或使用它们。追踪这些问题的答案可能很困难，用户为用户完成任务。预计移动代理技术将释放他们必须这样做。取而代之的是，“智能”移动代理将理解用户的要求，搜索网络自动地节点以获取适当的信息和服务，然后返回答案。但是，必须在我们期望代理人有效执行此类行动之前解决一些问题。我们专注于智力问题，作为代理功能的先决条件。代理商期望什么样的智能？我们已经根据制定灵活计划的能力采用了模型。具体来说，我们认为移动代理必须能够：了解用户需求；计划根据计划满足要求法的计划；根据实际条件与最初预期的条件不同时修改计划；并执行修改后的计划。我们已经在典型系统中实施了这些功能，并在几个示例应用程序中验证了它们的有效性。我们描述了如何将这些计划功能与移动代理设施相结合，并在个人旅行帮助的示例中展示了代理商如何智能行为。
Network environments give computer users the option of employing distributed information and services to complete a task. However, gathering information and using services distributed in networks requires knowing exactly what kinds of information and services are required for a task, where they are, and how they can be obtained or utilized. Tracking down the answers to these questions can be difficult, time consuming tasks for users. Mobile agent technology is expected to release them from having to do so. Instead, "intelligent" mobile agents will comprehend the user's requirements, search network nodes autonomously for appropriate information and services, and return with the answers. But several problems must be solved before we can expect agents to perform such actions effectively. We focus on the question of intelligence as a prerequisite for agent functions. What sort of intelligence is expected of agents? We have adopted a model based on the ability to make flexible plans. Specifically, we think mobile agents must be able to: understand user requirements; plan actions that will satisfy the requirements act according to the plan; modify the plan according to actual conditions when they differ from those initially expected; and execute the modified plan. We have implemented these functions in the Plangent system and validated their effectiveness in several example applications. We describe how we combined these planning functions with mobile agent facilities, and show how the agents behave intelligently in an example application of personal travel assistance.