Conversational Case-Based Planning for Agent Team Coordination
Conversational Case-Based Planning for Agent Team Coordination
复制标题
基于对话案例的座席团队协调规划
DOI：
10.1007/3-540-44593-5_14
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发表时间：
2001
期刊：
影响因子：
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通讯作者：
K. Sycara
中科院分区：
文献类型：
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作者：
J. Giampapa;K. Sycara
本文描述了一个原型，其中一个会话的基于案例的推理机，NaCoDAE，被代理，并插入到RET-SINA多Agent系统。它的任务是确定代理人的角色在一个异构社会的代理人，其中代理人可以使用基于能力或面向团队的代理协调策略。将此任务分配给NaCoDAE有三个原因：（1）减轻代理自己确定是否应该参与任务的开销;（2）将看似无关的数据转换为上下文相关的知识-作为基于案例的推理系统，NaCo-DAE特别适合将明显不连贯的数据应用于各种特定领域的情况;和（3）作为一个会话的CBR系统，既不引人注目地听取人类的陈述，并积极主动地与其他代理人在一个更有目的的方法来收集相关信息的对话。由NaCoDAE维护的案例具有问题和答案组件，最初旨在为人类维护问题和答案的文本表示。通过将代理能力描述和查询与案例问题相关联，NaCoDAE还承担了基于能力的协调者的团队角色。通过编码HTN计划目标的片段在其情况下的行动，我们能够转换成一个会话的情况下，基于规划师，服务组成生成的HTN计划目标，已经填充了与情况相关的知识，使用的RETSINA团队为导向的代理。
This paper describes a prototype in which a conversational case-based reasoner, NaCoDAE, was agentified and inserted in the RET- SINA multi-agent system. Its task was to determine agent roles within a heterogeneous society of agents, where the agents may use capability- based or team-oriented agent coordination strategies. There were three reasons for assigning this task to NaCoDAE: (1) to relieve the agents of the overhead of determining, for themselves, if they should be involved in the task, or not; (2) to convert seemingly unrelated data into contextually relevant knowledge — as a case-based reasoning system, NaCo-DAE is particularly suited for applying apparently incoherent data to a wide variety of domain-specific situations; and (3) as a conversational CBR system, to both unobtrusively listen to human statements and to proactively dialogue with other agents in a more goal-directed approach to gathering relevant information. The cases maintained by NaCoDAE havequestionandanswercomponents, which were originally intended to maintain the textual representations of questions and answers for humans. By associating agent capability descriptions and queries with the case questions, NaCoDAE also assumed the team role of a capability- based coordinator. By encoding fragments of HTN plan objectives in its case actions, we were able to convert NaCoDAE into a conversational case-based planner that served compositionally-generated HTN plan objectives, already populated with situation-relevant knowledge, for use by the RETSINA team-oriented agents.