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[Report] An Overview of Aritficial Immune SystemsClipped from: http://www.artificial-immune-systems.org/Courses/York/lecture1.pdf |
Short Lecture Course supplied by Jon Timmis of the University of York, UK (if you use any slides here, please provide a suitable acknowledgment). These lectures form part of a longer course on swarm intelligence, and also assume some knowledge of evolutionary computation. Immune networks are not covered in these slides, though they are on the course. The reason why there are no slides on immune networks is the topic is worked through with the students on a white board by adapting a clonal selection algorithm into an immune network algorithm. Negative selection is also covered on the white board, rather than through lecture slides.
第一讲：人工免疫系统概述
Slide的题目是
Collaborative Bio-Inspired Algorithms
Lecture 6: Artificial Immune Systems
Prof Jon Timmis
October 25, 2010
collaborative
adj. 合作的, 协力完成的
Outline:
What are AIS?
一种定义：AIS是一种受理论免疫学（theoretical immunology）及实验观察得到的免疫功能、原理、模型启发的适应性系统，被应用于多种复杂问题领域。（该定义来自于2002发表的书）
De Castro, L. & Timmis, J. Artificial immune systems: a new computational intelligence approach Springer Verlag, 2002
但是上述的定义已经不能“与时俱进”了，当前发生了一些变化。具体的将在本次lecture中提到。
History
提出问题！
But how do we manage this intreaction to make it worth while for all concerned … ?
接着提出了AIS一些有趣的地方，自适应性、无中心、自学习、动态平衡等特征。
AIS的概念框架(Conceptual Frameworks）
该框架图出自Timmis Jonathan与De Castro合著的书《Artificial immune systems: a new computational intelligence approach》。将当前研究的交叉方法简单描述出来。
这时候枪头一转，问个问题：什么是免疫系统（Immune System, IS）？
有三种不同角度的解释：
Classic view 代表的是clonal slection 和negative selection等方法。
AIS的工程应用
提到的论文，及其相关信息和摘要：
[1]Bersini, H. & Varela, F. Schwefel, H.-P. & Männer, R. (Eds.) Hints for adaptive problem solving gleaned from immune networks Parallel Problem Solving from Nature, Springer Berlin / Heidelberg, 1991, 496, 343-354
[2] Cohen, I. R. On Tending Adam’s Garden Tending Adam’s Garden, Academic Press, 2000, 243 – 255
[3] Cohen, I. R. Real and artificial immune systems: computing the state of the body Nature Reviews Immunology, 2007, 07, 569-574
[4] De Castro, L. & Timmis, J. Artificial immune systems: a new computational intelligence approach Springer Verlag, 2002
[5] Farmer, J. D.; Packard, N. H. & Perelson, A. S. The immune system, adaptation, and machine learning Physica D: Nonlinear Phenomena, 1986, 22, 187 – 204
Abstract: The immune system is capable of learning, memory, and pattern recognition. By employing genetic operators on a time scale fast enough to observe experimentally, the immune system is able to recognize novel shapes without preprogramming. Here we describe a dynamical model for the immune system that is based on the network hypothesis of Jerne, and is simple enough to simulate on a computer. This model has a strong similarity to an approach to learning and artificial intelligence introduced by Holland, called the classifier system. We demonstrate that simple versions of the classifier system can be cast as a nonlinear dynamical system, and explore the analogy between the immune and classifier systems in detail. Through this comparison we hope to gain insight into the way they perform specific tasks, and to suggest new approaches that might be of value in learning systems.
[6] Forrest, S.; Perelson, A.; Allen, L. & Cherukuri, R. Self-nonself discrimination in a computer Research in Security and Privacy, 1994. Proceedings., 1994 IEEE Computer Society Symposium on, 1994, 202 -212
Abstract: The problem of protecting computer systems can be viewed generally as the problem of learning to distinguish self from other. The authors describe a method for change detection which is based on the generation of T cells in the immune system. Mathematical analysis reveals computational costs of the system, and preliminary experiments illustrate how the method might be applied to the problem of computer viruses
[7] Hunt, J. E. & Cooke, D. E. Learning using an artificial immune system Journal of Network and Computer Applications, 1996, 19, 189 – 212
Abstract: In this paper we describe an artificial immune system (AIS) which is based upon models of the natural immune system. This natural system is an example of an evolutionary learning mechanism which possesses a content addressable memory and the ability to forget little-used information. It is also an example of an adaptive non-linear network in which control is decentralized and problem processing is efficient and effective. As such, the immune system has the potential to offer novel problem solving methods. The AIS is an example of a system developed around the current understanding of the immune system. It illustrates how an artificial immune system can capture the basic elements of the immune system and exhibit some of its chief characteristics. We illustrate the potential of the AIS on a simple pattern recognition problem. We then apply the AIS to a real-world problem: the recognition of promoters in DNA sequences. The results obtained are consistent with other appproaches, such as neural networks and Quinlan’s ID3 and are better than the nearest neighbour algorithm. The primary advantages of the AIS are that it only requires positive examples, and the patterns it has learnt can be explicitly examined. In addition, because it is self-organizing, it does not
require effort to optimize any system parameters.
[8] Ishida, Y. Fully distributed diagnosis by PDP learning algorithm: towards immune network PDP model Neural Networks, 1990., 1990 IJCNN International Joint Conference on, 1990, 777 -782 vol.1
Abstract: Based on the strong analogy between neural networks and distributed diagnosis models, diagnostic algorithms are presented which are similar to the learning algorithm used in neural networks. Diagnostic implications of convergence theorems proved by the Lyapunov function are also discussed. Regarding diagnosis process as a recalling process in the associative memory, a diagnostic method of associative diagnosis is also presented. A good guess of diagnosis is given as a key to recalling the correct diagnosis. The authors regard the distributed diagnosis as an immune network model, a novel PDP (parallel distributed processing) model. This models the recognition capability emergent from cooperative recognition of interconnected units.
[9] Matzinger, P. The Danger Model: A Renewed Sense of Self Science, 2002, 296, 301-305
[10] Neal, M.; Stepney, S.; Smith, R. & Timmis, J. Conceptual frameworks for artificial immune systems International Journal on Unconventional Computing, Old City Publishing, 2005, 1, 315-338
[11] Varela, F.; Coutinho, A.; Dupire, B. & Nelson, N. Cognitive networks: immune, neural and otherwise Addison Wesley Publishing Company, 1988
清明，对于中国人来说，是缅怀先人、逝去好友的时候。按照《岁时百问》一书中对清明的解释是：”万物生长此时，皆清洁而明净，故谓之清明”。看来正是这种最稚嫩、最新鲜的生命力迸发的时候，容易让人想起那些逝去的人。
这时候，我想起了外婆。外婆在前年秋天离开了我们，没能见到她最后一面，悔恨至今。与外婆在一起印象最深的有四个场景，终生不敢忘。
从记事起，自己家就和外婆家住的很远，那时候交通不是很方便，乘坐那种盘山路上的汽车，需要五六个小时。外婆家在小乡村，农户都喜欢养些土鸡，父亲那时候就和其他小商贩一起跑个短途，收购这些土鸡到城里贩卖。那时候家里经济是很紧张的，父亲很忙，经常会到外婆那边收购土鸡，我只要是周末就会和姐姐一起跟着父亲一起去，我们是去外婆家玩，父亲是去收土鸡。有时候父亲跑得勤，每天都跑的话，我们就在外婆家过夜，第二天再随父亲回去，跑得不勤则会当天回家。那时候还很小，似乎还是幼儿园的时候，外婆那里的条件还不好，晚上经常会限电，即使不限电的时候，也顶多开一盏昏黄的白炽灯。就在这样昏暗的光线下，外婆给我们做晚饭，在吃饭的时候点着油灯，免得看不清菜，呵呵。小时候一点也不觉得苦，一是还小，没有很多活需要干，二是很黏外婆，和外婆在一起，真的是什么都好。
附上微薄里面看到的一副图画和文字”你没出生，她最祈盼；你降生人间，她飞奔而来；你无论对她撒娇或耍脾气她都会冲你笑；你淘气闯祸，家里人谁骂你打你，她就会教训谁；她是你最想报答的人中，最难报答的人，在你事业有成有能力报答时，她可能已离你而去；她是你认为的伟大的人所认为的最伟大的人–她，就是外婆。”。
第二个场景是外婆家的大屋有个临街的大厅，农村经常有赶圩的活动，现在也都还有，小时候的赶圩比现在热闹得多，因为小时候物质资源还是挺匮乏的，十里八乡的都会趁着赶圩来卖自己的特产、土货，或者换些平时不好买到的东西，比如衣架、衣服、洋钉子等等现在的孩子无法想象的东西。现在这些东西哪里都容易买到了，所以现在赶圩的日子里人就少了很多。小时候赶圩的日子人很多，外婆就会做点豆浆、包子油条的卖给赶圩的乡亲。乡亲们赶圩都是大早出门，天都没亮，很多人都没吃早饭。所以那时候外婆的生意还是不错，乡亲们也都愿意来吃。我知道的是味道很好，价钱应该也不贵吧？反正我小时候怎么会关心这个呢？这个场景记得清楚，主要还是因为外婆家的大屋平时都没什么人来的，赶圩的时候人特多，在加上不听外婆的话，偷偷跑出去，在人山人海里面钻来钻去，看着各种新鲜玩意，所以就记住啦。
后面两个场景就比较悲伤了。但这不就是生活吗？10岁那样，父亲去收土鸡回来的时候乘坐的汽车翻了，再也回不来了。哭过后，小孩子还是又跑又闹起来了，没心没肺的吧？其实是不愿提起而已。父亲去世后，家里更艰难了，去外婆家自然就更少了，一般就寒暑假的时候去一下，我想母亲也是跑看管不过来，就把我扔外婆那了吧。不记得是10岁那年的暑假还是11岁那样了，我在外公的小阁楼里面翻出很多东西，有好多书（古典名著、数学谜题、天文地理都有一些），还有一些生锈的红缨枪。那时候，男孩子还不得乐死了，除了把这些书都看一遍外，自然要挑把红缨枪来耍耍咯。于是偷偷磨厉了枪头，奔向了户外，鬼哭狼嚎地跑着田野里、山间中。外婆那里的方言和我家是不一样的，我只听得懂，但是不会说，所以很外婆家邻居小孩都玩不来，只有舅舅的儿子和我玩，但是我们沟通还是要说普通话，他后来也很少和我玩了，呵呵。我就一个人在山间里、田野上跑啊跑啊，和水牛、花朵说话，把长得丑的树藤当成妖精来除。逍遥在天地之间，漫山遍野都和我在一起。说了这么多，其实是为自己后面闯祸埋个伏笔而已，哈哈。手持红缨枪，行走到一片连绵的红藕田，正值荷花烂漫之时。我虽是凡夫俗子一枚，但是尚有爱美之心，忍不住用红缨枪割取下一朵红莲，当时居然没有想到这乃有主之物，只当是天生地养的自然造物。当是没有想到是农户辛苦养造，乃是他们一家口粮所在。摘了一朵红莲，就少一朵莲蓬，农户就少了一份收入。之所以还有下文，那是因为当时被抓了个现行，不过似乎那农户认得我，晓得我是丧父之人……，收了红莲，放我去了。当时以为事情也就了了，谁知中午回去饭后午睡时候被人找上门了，依稀记得的就是”缺少爹娘教养”，哈哈哈，小爷当时很郁闷，什么话都没说，转身跑回床上偷偷流泪，哭着哭着就睡着了。说出来还真是不好意思。当时在床上躲着的时候，似乎听到外婆也发火了，冲着那上门来的邻居发火，后来似乎就是赔了点钱给他。外婆没有说我一句话，我想她心里是很难受的，心疼我这个刚丧父不久的坏孩子。外婆以为我是想吃莲子呢，那天下午给我弄了一碗莲子汤。我真不好意思和外婆说，我是喜欢那造物的红莲。这个场景，以前从未和人说起过，借着这个清明，我想说：外婆，很想你。
最后一个场景是在08年的夏天，那时候还在国软做项目，一个上午突然很想外婆了，因为那时候她身体就很不好了，我给外婆打电话，问她身体。外婆在电话里面还是很精神的。我记得当时和外婆说：我就快硕士毕业了，今年秋天开始找工作。外孙我能力还可以，可以找到好工作。我的目标是找到一个月1万以上的薪水的工作，我就有很多钱给外公外婆买好吃的，请外公外婆到外面去玩。
外公外婆其实很喜欢到名山大川、大城市看看的。当时外婆听了可高兴呢。后来阴差阳错选择了读博，没能兑现，悔恨不已！
大姐说过，父母在不远游，子欲养而亲不待，是人生中难以挽回的遗憾。好好珍惜和亲人们在一起的时候吧。
读那么多书，是为了完善自身，是为了寻求生命的真谛；工作那么努力是为了实现自身价值，获得自身满足。但是别忘了，亲人，生命中最亲的人，和他们一起享受短暂的人生也许才是真正的生命真谛！
Jackey's 感悟 
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