Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests

David Meyer <dmm@1-4-5.net> Wed, 29 March 2017 14:25 UTC

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From: David Meyer <dmm@1-4-5.net>
Date: Wed, 29 Mar 2017 07:25:40 -0700
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Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests
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Apparently you can't attach a .pptx. The attachment is here (pptx and pdf):

http://www.1-4-5.net/~dmm/ml/misc/musings.pptx
http://www.1-4-5.net/~dmm/ml/misc/musings.pdf

Thx,

Dave


On Wed, Mar 29, 2017 at 7:17 AM, David Meyer <dmm@1-4-5.net> wrote:

>
> Hey Min-Suk,
>
> Totally agree we need to learn from our environment, and RL is a natural
> approach. After all, the network is always changing, has adversaries, etc.
> All of this means. among other things,  that we can't make simplifying
> assumptions like stationary distributions,  iid data, .... So RL is one way
> to attack these problems, and the classic algorithms you mention below are
> certainly a reasonable approach (I've been working with policy gradients
> [0], trying to model/adapt the two-player game approach of AlphaGo to
> networking; the problem there is that we don't have a source of labeled
> expert data like the KGS Go server (https://www.gokgs.com/) to build the
> supervised policy network....).
>
> You might also want to check out the recent "boot" of evolution strategies
> as a black-box approach to RL (in particular no gradients). See [1],  [2],
>  [3]. There is also a ton of code around if you want to try some of this
> out (see e.g.,https://github.com/dennybritz/reinforcement-learning; this
> one is in tensorflow). Finally, I've attached a few summary slides with
> some of my musings on this topic from past talks.
>
> Thanks,
>
> Dave
>
> [BTW, two player minimax games seem to be popping up everywhere: AlphaGo,
> variational autoencoders [4], GANs [5], and many others; something to thing
> about for our domain]
>
> [0] https://papers.nips.cc/paper/1713-policy-gradient-
> methods-for-reinforcement-learning-with-function-approximation.pdf
> [1] https://blog.openai.com/evolution-strategies/
> [2] https://arxiv.org/pdf/1703.03864.pdf
> [3] http://jmlr.csail.mit.edu/papers/volume15/wierstra14a/wierstra14a.pdf
> [4] http://www.1-4-5.net/~dmm/ml/vae.pdf
> [5] https://arxiv.org/pdf/1406.2661.pdf
>
> On Tue, Mar 28, 2017 at 4:04 PM, 김민석 <mskim16@etri.re.kr> wrote:
>
>> Hi Brian,
>>
>>
>> As you mentioned by the prior email, anticipating network DDos
>> attacks is really trendy issue to solve by ML techniques.
>>
>> We also make some efforts how to avoid fagile nodes by a trustworthy
>> communication, that means quantifying trustworthiness of node with
>> normalization of various requirements such as security function, bandwidth
>> and etc.
>>
>> We are freshly approaching in routing layer with confidence using our own
>> requirements, TPD(Trust Policy Distribution) and TD(Trust Degree). These
>> requirements are considered to be solved by Reinforcement Learning
>> (RL) that is one of the ML algorithms. RL is useful to control some of
>> network policy about specific actions and states with reinforced and
>> purnished rewards (+/-), but the problem is too slow to acquire satisified
>> performance. Other ways to say it, anormaly dectection and regression
>> analysis might be both efficient approaching methods to solve the issues
>> Dave mentioned.
>>
>>
>> Best Regards,
>>
>>
>> Min-Suk Kim
>>
>> Senior Researcher / Ph.D.
>> Intelligent IoE Network Research Section,
>> *E*lectronics and *T*elecommunications *R*esearch *I*nstitute (*ETRI)*
>> e-mail          :  mskim16@etri.re.kr <nskim@etri.re.kr>
>> http://www.etri.re.kr/
>>
>>
>>
>>
>>
>>
>>