
The AI boss that deploys Hong Kong's subway engineers - chrisacky
http://www.newscientist.com/article/mg22329764.000-the-ai-boss-that-deploys-hong-kongs-subway-engineers.html#.U7hGpPldWy5
======
idunning
This seems like a pretty standard application of
scheduling/optimization/operations research. Other applications include:

\- Crew scheduling for airlines (e.g. how to satisfy union rules/minimize
time-on-ground/get people back home)

\- Container flow at ports

\- Railway yard scheduling (e.g. allocating cars to engines)

For another example of this particular kind of work (allocating maintenance
staff to projects), see a colleague's paper on work for a large gas/electric
utility:
[http://stuff.mit.edu/people/uichanco/scheduling.html](http://stuff.mit.edu/people/uichanco/scheduling.html)

Calling this AI is really a stretch, although the definition of AI is fairly
loose I suppose. All the "smarts" are entered by humans in the form of
constraints (rules, in this article), the computer's role is essentially just
very efficiently searching a combinatorial search space for the best solution.
Integer linear programming is a common approach for getting optimal solutions,
but can require deep expertise to make fast enough. It seems in this case they
have settled for heuristic solutions from a genetic algorithm - perfectly
reasonable approach.

The real story for any system like this is actually getting humans to buy into
it, see e.g. the many articles about the UPS ORION system for more coverage on
this. The last paragraph hints at this but doesn't talk about how they got
buy-in, which is something that would be really interesting.

~~~
fryguy
I see this as an extrapolation of an expert system, which is an AI. It seems
the more you know about how they work, the less they seem like artificial
intelligence and more like some simple optimization problem. I suppose this is
what the Chinese Room
([http://en.wikipedia.org/wiki/Chinese_room](http://en.wikipedia.org/wiki/Chinese_room))
is all about.

~~~
idunning
I don't think it truly qualifies as an expert system because, to the best I
can determine from this limited article, it doesn't make any inferences from a
base set of rules. Rather, it is provided with a comprehensive set of all
rules, and attempts to find the best solution that satisfy those rules (or, in
the language of operations research, constraints). The value provided then is
that, because it can consider the space of solutions efficiently, it may
discover solutions which are not obvious to human operators.

The way these systems are made, and as the article mentions, is that the
obvious rules are added, and solutions evaluated. Experience humans will then
usually discover something about the solution that is impossible/impractical,
but was not included in the provided rules. The new rules/constraints, and the
process repeats until humans are comfortable with the feasibility of the
solution/schedule.

~~~
cpeterso
That's a good point. This system is more of a constraint solver than an expert
system. It's not really answering questions than optimizing one (big and
dynamic) scheduling problem.

------
cseelus
This really reminds me of an essay called "Manna" from Marshall Brain (which
may already be known to a large number of HN readers).

[1] [http://marshallbrain.com/manna1.htm](http://marshallbrain.com/manna1.htm)

[2]
[https://en.wikipedia.org/wiki/Manna_(novel)](https://en.wikipedia.org/wiki/Manna_\(novel\))

~~~
nodata
Definitely (if in need of an editor).

------
mmv
SISCOG [1] is a company that has been developing such systems for quite some
time now (since 1986).

In their case I know that the core of their systems is written in LISP and
it's mostly AI optimizaiton algorithms. The guys who run the company are both
professors of AI related classes in the CS dept of IST.UTL.PT.

[1]: [http://siscog.pt/](http://siscog.pt/)

~~~
ghiculescu
[http://biarri.com](http://biarri.com) too. They use c++ and python I think,
and a few of the guys there lecture at the University of Queensland.

I don't think this sort of scheduling optimisation is as unique as the OP
makes out.

------
xophe
>>But the people that had to carry out the scheduled work took a while to get
used to the idea, as they didn't like not knowing why they were doing certain
things.

There's some real Asimovesque worship of technology here. Without sounding
like a Luddite (computers are good at scheduling), this is scientifically
_scary_.

~~~
saraid216
> Strike the first rune upon the engine's casing employing the chosen wrench.
> Its tip should be anointed with the oil of engineering using the proper
> incantation when the auspices are correct. Strike the second rune upon the
> engine's casing employing the arc-tip of the power-driver. If the second
> rune is not good, a third rune may be struck in like manner to the first.
> This is done according to the true ritual laid down by Scotti the Enginseer.
> A libation should be offered. If this sequence is properly observed the
> engines may be brought to full activation by depressing the large panel
> marked "ON".

~~~
omegaworks
Post apocalyptic technology worship: A Canticle for Leibowitz by Walter Miller
:)

The world needs more of this stuff.

------
mistermcgruff
Sounds more like a classic optimization model (operations research) than like
an AI model. Great case study though. I wonder why they used a GA and custom
stuff instead of off the shelf software from gurobi or IBM OPL etc.

~~~
saosebastiao
For scheduling problems, I have had way more luck with open source CP solvers
like Choco than I have had with hyper-optimized commercial IP solvers like
gurobi. Branch and Cut is just too indirect for constraint heavy IP models.

~~~
idunning
"Nurse scheduling competitions" seem to alternate between IP and CP approaches
- my feeling is that when the number of solutions is "small", CP pulls ahead,
but when there are many solutions and the objective is important, IP wins.
Very case-by-case dependent, regardless.

~~~
saosebastiao
I've found that size doesn't matter much at all...but constraint type does
(hard vs soft). Soft constraints very easily lend themselves to IP
formulations, but problems with lots of hard constraints require a ton of luck
to get it to work quickly with an IP solver. 3d bin packing, for example, is
very heavy on hard constraints. I have a CP model (using the Geost constraint
primarily) with an ensemble search heuristic (genetic + LNS), and I have yet
to find an IP model that can get within 2 orders of magnitude of the average
solve times of the CP model.

------
superuser2
From the article, it doesn't sound like any actual artificial intelligence or
machine learning techniques are being applied here, just a collection of
algorithms. Does anyone know technical details?

~~~
jrockway
They said it's a genetic algorithm, which just means they're looking for an
approximate solution to an NP complete problem. My guess is that they have a
list of tasks and dependencies, and then try to do the tasks in an order that
minimizes conflicting use of dependencies. An example being track section A
requires signal work and track work. section B requires track work. Do the
signal work on section A and the track work on section B, then do the track
work on section B. This is better than doing the track work on section A and
letting section B remain idle.

Of course, with hundreds of miles of track and thousands of tasks to perform,
the algorithm does a better job than people. Though people are not
particularly bad at this task.

~~~
lifeisstillgood
I also suspect this is a "defensive" move. A good experienced co-ordinate will
give a ML system a run for its money on "gut" plus be able to factor in the
efficiency of certain teams, morale and even how hard the "track work" is -
but he gets it wrong and it was "gut" and his boss cannot justify it. Put in
an ML system and his boss now gets to say "the system decided"

Both ways there is an expected % of sub optimal decisions but only one can the
boss blame someone else.

~~~
TeMPOraL
Thing is, computers are really better at such tasks when the number of
constraints grow too much for human to handle (i.e. beyond a few). It's
because in time the human expert is able to articulate the solution he just
thought of, the computer will search 100 000 other points in solution space
and likely find a better one.

~~~
lifeisstillgood
But ... Only if a programmer has coded the other solution into the algorithm
OR if the ML is able to distinguish between a correlation and a causation.

Before Looking for Black Swans look for Xmas Turkey's.

~~~
TeMPOraL
> _if the ML is able to distinguish between a correlation and a causation_

Of course it is. You can get the causation structure out of a correlative
dataset with some basic Bayesian math.

[http://lesswrong.com/lw/ev3/causal_diagrams_and_causal_model...](http://lesswrong.com/lw/ev3/causal_diagrams_and_causal_models/)

------
matthewrudy
This is quite an exciting change from the usual government bungling (the MTR
is 75% owned by the government)

I'd like to see some of this technology make its way out the MTR and
invigorate HK tech in general.

------
jamesbritt
This posted not too long ago.
[https://news.ycombinator.com/item?id=7992109](https://news.ycombinator.com/item?id=7992109)

Got no traction though.

------
sandGorgon
Does anyone know how these things are deployed in real life/production ? I
know of Choco as a solver, but have only used it in commandline programs.

Does this kind of a system manifest itself as a webapp or as a dedicated
Windows box with a GUI. Are the "machine readable rules" merely saved in the
database - does anyone know how these look like in terms of schema ?

------
blooberr
Reminds me of this well-written short story (part 1):
[http://marshallbrain.com/manna1.htm](http://marshallbrain.com/manna1.htm)

------
orbitingpluto
My first thought was, "Primordia?"

But really, I think the term AI is a bit much here.

------
waps
This article gives an example of which are the easiest jobs to replace by
AI/programs : middle management. Any manager that merely manages a few people
and doesn't take responsibility for any business function ... that's what's
getting replaced first.

Tbh, I won't miss these guys at all.

On the other hand, this is the future. You want to talk to your boss ? Well he
has 500 reports, so "press 1 to ask for a day off". I do believe they have the
potential to be much more flexible than any human though. Of course that's
going to be exploited in favor of businesses.

But if my job was essentially to pass on information from management to
individual contributors, I'd be very worried.

~~~
vidarh
If your job is essentially to pass on information from management to
individual contributors, then irrespective of your title, you are not "middle
management", you're a secretary.

