摘要翻译：
本文介绍了一种新的通信网络路由表自适应学习方法AntNet。AntNet是一个基于移动代理的分布式蒙特卡罗系统，它是受最近关于解决优化问题的蚁群隐喻的启发而设计的。ANTNET的代理同时探索网络并交换收集到的信息。代理之间的通信是间接的、异步的，由网络本身中介。这种交流形式是典型的社会性昆虫，被称为污名。我们将我们的算法与来自电信和机器学习领域的六种最先进的路由算法进行了比较。算法的性能在一组真实的测试床上进行了评估。我们在实际和人工IP数据报网络上进行了大量的实验，在不同的空间和时间流量分布下，节点数目不断增加。结果非常令人鼓舞。与竞争对手相比，AntNet在所有实验条件下都表现出了优越的性能。我们分析了该算法的主要特点，并试图解释其优越性的原因。
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英文标题：
《AntNet: Distributed Stigmergetic Control for Communications Networks》
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作者：
G. Di Caro, M. Dorigo
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  This paper introduces AntNet, a novel approach to the adaptive learning of routing tables in communications networks. AntNet is a distributed, mobile agents based Monte Carlo system that was inspired by recent work on the ant colony metaphor for solving optimization problems. AntNet's agents concurrently explore the network and exchange collected information. The communication among the agents is indirect and asynchronous, mediated by the network itself. This form of communication is typical of social insects and is called stigmergy. We compare our algorithm with six state-of-the-art routing algorithms coming from the telecommunications and machine learning fields. The algorithms' performance is evaluated over a set of realistic testbeds. We run many experiments over real and artificial IP datagram networks with increasing number of nodes and under several paradigmatic spatial and temporal traffic distributions. Results are very encouraging. AntNet showed superior performance under all the experimental conditions with respect to its competitors. We analyze the main characteristics of the algorithm and try to explain the reasons for its superiority.
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PDF链接：
https://arxiv.org/pdf/1105.5449