Emergence of communication for negotiation by a recurrent neural network
Emergence of communication for negotiation by a recurrent neural network
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通过循环神经网络进行谈判通信的出现
DOI：
10.1109/isads.1999.838450
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发表时间：
1999
期刊：
影响因子：
--
通讯作者：
Koji Ito
中科院分区：
文献类型：
--
作者：
K. Shibata;Koji Ito
我们认为多智能体系统中的通信有两个主要的含义。其中之一是将一个代理观察到的信息传递给另一个代理。另一个含义是传递agent的想法。在此，我们将重点关注后者，并致力于在一些代理之间出现自治和分散的仲裁通信。通信内容、策略和表示都不是预先规定的，而是通过学习获得的，在智能体行动后给予强化信号。强化信号不与其他智能体共享。为了实现这种学习，智能体不仅要从现在的通信信号中做出决定，还要从过去的信号中做出决定。在此基础上，提出了基于递归型（Elman）神经网络的系统架构。通过二代理和四代理协商问题检验了该体系结构的能力。为了避免决策后的冲突，代理之间通过学习产生了多种谈判策略。
We believe that communication in multi-agent system has two major meanings. One of them is to transmit one agent's observed information to the other. The other meaning is to transmit what an agent is thinking. Here we focus the latter and aim to the emergence of the autonomous and decentralized arbitration communication among some agents. Communication contents, strategy and representation are not prescribed and are acquired by learning using a reinforcement signal which is given to the agent after its action. The reinforcement signal is not shared with the other agents. In order to realize this learning, the agent often has to make a decision not only from the present communication signals but also from the past signals. Accordingly the system architecture using recurrent type (Elman) neural network is proposed. The ability of this architecture was examined by two and four agents negotiation problems. A variety of negotiation strategies emerged among the agents through the learning to avoid some conflict after their decisions.