[Haskell-cafe] Reactive Programming in Machine Learning

[David Barbour]

I have spent several months studying use of generative grammars in
multi-agent reactive systems [1] - granted, not FRP in particular, but RDP
is reasonably close [2]. This result is, implicitly, a distributed,
federated machine-learning system (briefly described at [3]). The 'learning'
supports rapid agreement between agents and eliminating the volatility seen
in a stateless reactive model.

Individual agents are simple; intelligence in this model emerges only as we
scale. It's a very simple learning model: individually, agents try to
generate grammars that that are (based on history) likely to be accepted by
other agents. Developers control what can be learned by specifying a
non-deterministic choice in the generative grammar (i.e. non-determinism is
seen as 'permission to choose and learn', not 'random').

By a simple extension of grammars with time (i.e. a grammar generates a
sentence that says not just what to do, but when to do it), I believe I can
achieve a huge level of intelligent coordination and cooperation between
agents. I.e. they'll automatically schedule their activities, and reactively
adjust to accommodate changes in plans or the introduction of new agents.

I tabled further study of this promising model until I sufficiently develop
RDP, which is far more suitable than FRP for open, scalable systems.

[1] http://lambda-the-ultimate.org/node/4012
[2] http://awelonblue.wordpress.com/2011/05/21/comparing-frp-to-rdp/
[3] http://lambda-the-ultimate.org/node/4012#comment-62877<http://lambda-the-ultimate.org/node/4012#comment-63105>

On Fri, Jul 22, 2011 at 11:30 AM, bob zhang <bobzhang1988 at gmail.com> wrote:

> Hi all,
> I am doing a survey on combining Functional Reactive Programming and
> Machine Learning. Has anyone did relevant research on this topic?
> Any discussion or link is appreciable.
> Best,bob
>
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