神经网络社团中的挫折、玻璃态行为和动态退火
Frustration, glassy behavior and dynamical annealing in societies of Neural Networks
摘要：
我们研究的最大熵机制的神经网络建模的代理人之间的信息交换和宏观状态的社会，这样的代理人在几种情况下。数学量化的惊喜，不信任其他代理人和信心的意见出现在熵为基础的学习动力学的基本成分。学习被证明是由惊喜驱动，即受体代理面临着一个不信任的代理或与一个受信任的代理的不同意的意见的一致意见。意外的责任归属来自于接收者对发射者代理人的不信任和接收者对自己观点的信心。这种动态主要通过一个或另一个的变化来进行：受体对问题的看法或对发射者的不信任。$N$智能体就一系列问题交换二元观点的社会表现出丰富的行为，这取决于议程的复杂性。对于小的集合，社会达到一个稳定的状态，两极分化成对立的派别，在那里，平衡的规范，如“敌人的朋友是敌人”得到严格的满足。对于更大范围的问题，社会可以在像旋转玻璃一样的状态下持续很长时间。有两种类型的挫折：意识形态和情感，与动态退火属性取决于所讨论的问题的复杂性，导致缺乏明确界定的长期瞬态当事人。
英文摘要：
We study maximum entropy mechanisms of information exchange between agents modeled by neural networks and the macroscopic states of a society of such agents in a few situations. Mathematical quantification of surprise, distrust of other agents and confidence about its opinion emerge as essential ingredients in the entropy based learning dynamics. Learning is shown to be driven by surprises, i.e. the receptor agent is confronted with the concurring opinion of a distrusted agent or with a trusted agent's disagreeing opinion. Attribution of blame for the surprise derives from measures of distrust of the receiver towards the emitter agent and the receiver's confidence about its own opinion. The dynamics proceeds by changes of mainly one or the other: the receptor opinion about the issue or the distrust about the emitter. A society with $N$ agents exchanging binary opinions about a set of issues show rich behavior which depend on the complexity of the agenda. For small sets the society reaches a steady state polarized into antagonistic factions, where balanced norms such as "the friend of an enemy is an enemy" are strictly satisfied. For larger sets of issues, societies can persist for a long time in spin-glass like states. There are two types of frustration: ideological and affective, with dynamical annealing properties depending on the complexity of the set of questions under discussion, leading to the lack of sharply defined parties for long transients.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
