Multi-Agent系统中Agent知识获取的合作模型

摘要

Knowledge is the precondition for an agent to compute. In dynamic and non-deterministic multi-agent systems, agent should be able to acquire the knowledge timely and effectively so as to solve the problems. The existing knowledge-acquiring models can't meet the knowledge-acquiring requirements in dynamic and non-deterministic multi-agent system and the agent's capability of acquiring knowledge is limited. The systematic knowledge-acquired cooperation models (KACM) is presented for agent to effectively acquire knowledge in multi-agent systems, including passive model, active terminating model and active non-terminating model. Based on the speech act theory and a formal framework of branch temporal logic, the communication acts in KACM are discussed, how agent responses to the communication acts is investigated, the rigorous semantics of the speech acts and the KACM are defined, and lastly the significance of the research is presented.%Agent的知识是Agent计算的前提.在动态、不确定的Multi-Agent系统中,Agent必须具备及时有效地获取所需知识的能力以求解问题.现有的知识获取模型不能有效地支持在动态、不确定的Multi-Agent系统中Agent对知识获取的要求,Agent的知识获取能力比较有限.提出一个系统的、用于Agent知识获取的合作模型KACM(knowledge-acquiring cooperation model)系列,包括被动模型、主动终止模型和主动非终止模型.基于言语行为理论和以分枝时序逻辑为基础的形式化框架,讨论了KACM所涉及的Agent通信行为,分析了Agent如何响应这些通信行为以完成知识交互,定义了各通信行为以及KACM的满足语义,最后讨论了研究工作的意义.