摘要翻译：
该模型的主要功能是访问UCI Wisconsin乳腺CAN-CER数据集[1]，并将数据项分为正常和异常两类。这种分类称为异常检测，它将计算机系统中的异常行为与正常行为区分开来。人工免疫系统(AIS)是一种常用的异常检测方法。人工免疫系统是受理论免疫学的启发，利用观察到的免疫功能、原理和模型，应用于问题求解的自适应系统。树突状细胞算法(DCA)[2]是一种专门用于异常检测的AIS算法。它已成功地应用于计算机安全中的入侵检测。智能Agent是免疫实体在AIS中的完美表达，因此基于Agent的建模方法是实现AIS的理想方法。该模型评估了在一个基于Agent的仿真环境AnyLogic中重新实现DCA的可行性，其中DCA中的免疫实体由智能Agent表示。该模型的成功实现，为在基于Agent的仿真环境中实现更加复杂和自适应的AIS模型提供了可能。
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英文标题：
《An Agent Based Classification Model》
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作者：
Feng Gu, Uwe Aickelin, Julie Greensmith
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最新提交年份：
2009
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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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一级分类：Computer Science        计算机科学
二级分类：Multiagent Systems        多智能体系统
分类描述：Covers multiagent systems, distributed artificial intelligence, intelligent agents, coordinated interactions. and practical applications. Roughly covers ACM Subject Class I.2.11.
涵盖多Agent系统、分布式人工智能、智能Agent、协调交互。和实际应用。大致涵盖ACM科目I.2.11类。
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英文摘要：
  The major function of this model is to access the UCI Wisconsin Breast Can- cer data-set[1] and classify the data items into two categories, which are normal and anomalous. This kind of classifi cation can be referred as anomaly detection, which discriminates anomalous behaviour from normal behaviour in computer systems. One popular solution for anomaly detection is Artifi cial Immune Sys- tems (AIS). AIS are adaptive systems inspired by theoretical immunology and observed immune functions, principles and models which are applied to prob- lem solving. The Dendritic Cell Algorithm (DCA)[2] is an AIS algorithm that is developed specifi cally for anomaly detection. It has been successfully applied to intrusion detection in computer security. It is believed that agent-based mod- elling is an ideal approach for implementing AIS, as intelligent agents could be the perfect representations of immune entities in AIS. This model evaluates the feasibility of re-implementing the DCA in an agent-based simulation environ- ment called AnyLogic, where the immune entities in the DCA are represented by intelligent agents. If this model can be successfully implemented, it makes it possible to implement more complicated and adaptive AIS models in the agent-based simulation environment.
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PDF链接：
https://arxiv.org/pdf/0910.2874