Where machine learning meets rule-based systems

https://blog.foretellix.com/2017/07/06/where-machine-learning-meets-rule-based-verification/

140 37 9 years ago

blog.foretellix.com

veselin• 9 years ago

This is indeed a very interesting topic. I am working in the area and we get some rule-based models perform as good as neural networks and overall there are good steps: http://www.srl.inf.ethz.ch/papers/charmodel-iclr2017.pdf

These are models that can be inspected by an expert (or even non-expert) and then analyzed. But I guess that we must go past the neural network hype. At the moment it is very common that if I mention machine learning to people, they correct me and say "deep learning" :)

sanxiyn• 9 years ago

This reminds me of Brill tagger, which learns rules to do part-of-speech tagging -- rules are learned, but execution is entirely deterministic application of rules.

exelius• 9 years ago

I honestly read this article as taking more of a philosophical stance than a technical one. The fact is that we as humans will require proof and verification of human control over a system is an inescapable one. Basically, on some level we need guarantees and proof that the machine will never come up with a rule that says "kill all humans".

This means that we'll need human-supplied rules, and any mature ML platform will need to be able to incorporate these rules. IMO it's a decent problem-based hypothesis as to what the future evolution of ML platforms will look like, but as with any hypothesis, it will need to be tested, proven wrong in some way, and adjusted accordingly.

yoav_hollander6• 9 years ago

OP here:

I indeed meant it in the philosophical sense you describe. But I am very interested in the possible technical solutions. I tried to describe (in the chapter "Connecting ML and rules") the approaches I know of, none of which are very exciting.

I'd love to hear if anybody knows of good approaches.

sharemywin3• 9 years ago

don't kill humans as a rule won't work just don't recognize that "meat-bag" over there as a human goal accomplished rule circumvented. or maybe it's lower which colors are skin colors? how do you define valid colors? to define human properties?

At the end of the day I think there is a reason for the old saying the exception that proves the rule.

joe_the_user• 9 years ago

Is there a hard boundary between "the philosophical" and "the technical"? Or rather, I don't think "the philosophical" is necessarily that useful a tool for understanding how to resolve the thorny question of "human control" of AI.

Human beings are very good at communicating our "intentions" through natural language. Such communication, however involves "good faith". So communication with an AI would seem to involve more than just following logical directive, the AI would need a model of a person which would allow it to deduce and follow the wants/desires/intentions behind a given group's humans' communication with it.

Of course, if we go down deeper, we can notice that these wants/desires/intentions themselves "don't really exist" in the sense that a single person generally has impulses that go in different directions in the general case and it is the way that they live in a stable society that keeps those contradiction from appearing (which brings to mind many "sausage and sausage factory" analogies - lots of people want the results of a process but would object if they knew everything that goes in - while vaguely knowing/hoping there is a bit of regulation keeping things not too far out of bounds).

Traditionally, philosophy is where conceptions of language, society and logic meet and supposedly get sorted-out. But unfortunately our traditional philosophy seems a woefully inadequate tool for the problems of language interactions coming out of social processes and we most likely should look at some more contemporary theories of language and society for this (standard philosophy especially arose strengthening the false intuition that an intention could exist as a "free-standing" rather than getting that interactive fabric that human society and just as much the idea that a phisolophical would not be "technical" illustrates the problems). My favorite for understanding this stuff instead, is evolutionary game theory.

PeterisP• 9 years ago

The word "need" in this comment seems a bit exaggerated. Yes, we'd want to have some proof and verification, but that is not an inescapable requirement.

I'm fairly sure that we as a society in general are willing to launch systems where we have only a reasonable expectation and trust that it will most likely work properly instead of total proof and verification that it definitely will do so. Just take a look at pretty much any life-critical system in use today; some verification and testing is required, but formal proof of correctness is a very high bar that's never required for practical systems. It's highly desirable, but it will only be an absolute requirement if it's reasonably easy to achieve.

I'm fairly sure that if it turns out that we can't provide guarantees and proof about what the machine might do, then we'll just do our best even if the result is not provable and verifiable and launch such systems anyway.

And I think that even if we have a choice between two systems where one is verified and proved that it never can do anything bad but is otherwise inferior in what it can do, and the second system has no such proof but seems safe in general testing and simply performs better than the first one.... then it's quite likely that we'll choose the second one anyway.

adamnemecek• 9 years ago

They are gonna merge I think. NNs will be used to extract rules.

sharemywin3• 9 years ago

Some Machine learning is rule based ie. Random forests or random decision forests

eanzenberg• 9 years ago

In this context rule-based usually means rules handed down by a person/people. Tree-based algo's create their 'rules' using the data only.

yoav_hollander6• 9 years ago

I do think that random forests (and Inductive Logic Programming) seem easier to connect to rule-based "human-written" logic.

It does seem though that Neural Networks are the main ML story, at least for now, by a wide margin.

abhgh2• 9 years ago

Probably relevant, there has been work on converting feed-forward networks to decision trees e.g. [1].

EDIT: why is this relevant to the parent comment? You can learn a neural net, convert it to a tree, and then integrate rules from domain experts.

[1] [PDF] https://www.aaai.org/Papers/FLAIRS/2004/Flairs04-089.pdf

YeGoblynQueenne5• 9 years ago

I enjoyed your article very much and it touches on many of my own interests, but if we got to the point where "it's not neural networks" is now a legitimate reason against trying some technique, then something, somewhere has gone really wrong.

abhgh2• 9 years ago

Note though that the moment you go beyond single decision trees, with say random forests, boosted trees or bagged decision trees, the models become less and less interpretable. They "look" like rules, but with multiple disjoint sets of them its hard to intuit how they interact.

daly• 9 years ago

On the TIRES project (http://daly.axiom-developer.org/cmu/book.pdf) we used Scone (http://www.cs.cmu.edu/~sef/scone/), a knowledge representation system as the core structure. Rules based on OPS5 (http://repository.cmu.edu/cgi/viewcontent.cgi?article=3430&c...) were used to do planning.

Scone concepts were linked to machine learning for two tasks, recognition (e.g. giving grounding to the concept 'wrench' as a recognizable object) and actions (e.g. giving grounding to concepts like tighten as a series of actions).

So ML was an "interface" for recognition and "compiled knowledge" where the system knew how to perform certain actions without any consultation with the rest of the knowledge (similar to how you can recognize and type words without thinking).

soVeryTired• 9 years ago

Conditional Random Fields?

Houshalter2• 9 years ago

This is a really important problem to solve, for more than just self driving cars. We have no good way of controlling AIs right now. Particularly reinforcement learners. All we can do is make a fitness function and hope they maximize it. Often they will find a way to exploit it in a way the programmer didn't expect or desire.

And even making a fitness function is a pretty difficult task. You can't exactly let a self driving car get into thousands of accidents until it learns not to do that. Making a good simulation is extremely difficult and very expensive. Training it to mimic humans means it will always be worse than humans.

As AIs get smarter this will become more of a problem. They will become more clever at exploiting the reward functions and even fooling human supervisors. One could imagine a self driving car causing accidents indirectly in some way. But it doesn't care because the car itself didn't collide with anything.

In the extreme case, AIs much smarter than humans would be extremely difficult to trust. They could figure out how to manipulate humans to get them to press their reward buttons or whatever equivalent. The famous thought experiment is an AI in a paperclip factory that desires to maximize paperclip production. So it invents nanotech, tears apart the Earth, and eventually turns the entire solar system into a massive paperclip factory.

Perhaps that's a long way off. But it's not comforting to know that AI ability is increasing faster than our ability to control AI.

cbames893• 9 years ago

"Training it to mimic humans means it will always be worse than humans." This is not necessarily true, there is work in learning from demonstration where robots have exceed the "experts" reward. I'll post some papers later if you're interested.

YeGoblynQueenne5• 9 years ago

Please do. I'm not the OP but I'm interested.

chongli• 9 years ago

Training it to mimic humans means it will always be worse than humans.

The success of AlphaGo says otherwise. The distinction is that you're not mimicking one human but some large set of humans and you're using your fitness function to guide you toward a maximum 'meta human'.

We don't need self driving cars to be perfect drivers. If they're better than any human driver then that's a good enough reason to replace human drivers.

akvadrako• 9 years ago

AlphaGo was not primarily trained to mimic humans, but to win games. This included playing many games against itself and semi-random tree searches for better strategies. If it was only mimicking humans it probably would have lost to the world's best.

Houshalter2• 9 years ago

AlphaGo was only initialized by mimicking humans. It then played millions of games against itself. And learned what moves were more likely to lead to wins. And then it was combined with a tree search algorithm that let it explore many more moves into the future than humans could consider.

Training a bot just to predict what move a human would make, would always lose to that human. It will just absorb all the human's mistakes and weaknesses. And on top of that it will add it's own, since no AI algorithm is anywhere near 100% perfect.

B1FF_PSUVM• 9 years ago

For a moment I thought this was making a parallel with 1980s "knowledge-base systems", which tried to make hay by piling up rules gleaned from experts.

Those systems sort-of, kind-of worked, and got plenty of press, but vanished from the market without much of an obituary, or post-mortem exam, AFAIK.

My guess is that they were impossible to maintain.

I'm waiting to hear that the problem is now licked for the current crop.

tumba• 9 years ago

There are many rule-based expert systems in large scale use today. I know of applications in medicine, supply chain and logistics, and military.

tjoff• 9 years ago

Quite popular for airplane autopilots too (both manned an unmanned).

YeGoblynQueenne5• 9 years ago

> Those systems sort-of, kind-of worked,

Rather more than that. For a famous example, check out the wikipedia article on MYCIN (a classic of the era):

MYCIN was never actually used in practice but research indicated that it proposed an acceptable therapy in about 69% of cases, which was better than the performance of infectious disease experts who were judged using the same criteria.

https://en.wikipedia.org/wiki/Mycin

There were many practical reasons why expert systems fell out of grace (although like others say they are still used widely, in real-world applications) but a large part of them was political, rather than anything to do with their effectiveness.

Some historical background:

https://www.researchgate.net/publication/3454567_Avoiding_An...

In the end, the problem with expert systems was probably that they didn't scale as well as one would hope -primarily because it's damn hard to develop and maintain huge hand-crafted databases of knowledge solicited by experts who have very little incentive to collaborate in the creation of a system that will put them out of work.

sixdimensional• 9 years ago

The title says it all - machine learning and rule-based systems are complementary tools, not mutually exclusive.

I have prior experience applying both to fraud detection, and for fraud detection, there are lots of regulations that are actionable as hard and fast rules running in an expert system.

There are also patterns of fraud that are not obvious, and data mining techniques along with machine learning are incredibly useful for detecting them.

More importantly, if you use a rule-based system first you can often use the output (for example, a rule-based score for a given input case) as an input into machine learning. So, for example, the rule-based system is used to help classify concrete cases, whereas the machine learning might help classify more "gray area" cases based on the hard and fast rules (depending on how you configure your algorithms).

hatmatrix• 9 years ago

Some of the instances where ML is brought in can instead be handled by fuzzy logic?

Where machine learning meets rule-based systems

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