by _448 on 5/25/23, 9:37 AM with 75 comments
by currymj on 5/26/23, 8:00 AM
If you want to take a system that's working on Boolean logic, and introduce uncertainty, replacing true/false with probabilities does a great job of this. However in the 1960s/1970s, people believed it was hopeless to have AI systems use probability to deal with uncertainty. This is because probability requires you to use Bayes' Theorem, and computing the denominator in Bayes' Theorem requires summing over an exponentially large number of different outcomes.
Thus people came up with all kinds of alternative systems to avoid dealing with probability. Among these would have been fuzzy logic.
However people came up with ways to cope with the computational intractability (belief propagation on Bayesian networks, better Markov-chain Monte Carlo algorithms, etc.), so probability became practically viable. And on the merits, if you can deal with the computational issues, probability seems to be much nicer than these other formal systems. So since the 1980s/1990s the probabilistic approach to AI has become dominant (even if deep learning has displaced the actual models).
by ojosilva on 5/26/23, 11:06 AM
Fuzzy didn't go very far because it could already be implemented back then with cheap processors (we had Moore's law!) and some slap-on programming. Or domain-specific logic hardwired by engineers who knew what the outcome should be given a bunch of inputs: think "balancing robot" or washing machine torque control.
Modern ML and powerful Arduinos make fuzzy logic irrelevant today, except that Markov chains should also be irrelevant but they aren't, so who knows, maybe fuzzy could stage a come back. Despite its huge limitations, FL certainly is better than current neural nets when it comes to transparency so that humans can better visualize what it's doing. Disclaimer: back in the day I built a "FL tracker" in Visual Basic so that you could visualize the full system trickle-down a decision in realtime and it was very enlightening. Today I'd love to build the same visualizaer but for LLM generation!
by nabla9 on 5/26/23, 8:03 AM
Fuzzy Logic has some utility in engineering applications, because it can be intuitive for humans interpret and to create rules using it. Interpretability is also the strong point of neuro-fuzzy logic (hybrid system). Applications are usually control systems, business logic, or embedded systems with tight resource constraints.
(negatives:)
In academic research it's not that interesting, because it's just one multi-valued logic among others, subset of infinite-valued logic. It's not very useful in machine learning because there are no efficient way to learn fuzzy rules automatically.
by h2odragon on 5/25/23, 10:38 AM
Thruly, tho look at things like QAM encoding schemes and the tricks stepper motor controllers are doing. We have found ways to analog-ize a lot of things that were formerly viwed as totally digital; which i think is what the "fuzzy logic" hype boiled down to anyway.
by dollo_7 on 5/26/23, 7:44 AM
by segmondy on 5/25/23, 7:12 PM
by captainmuon on 5/26/23, 9:19 AM
When I'm drawing on screen and placing a widget not on the left or the right, but at x=f*width, is that already fuzzy logic? The classical examples of Japanese washing machines determining detergent dose are not much more complicated.
by PinguTS on 5/26/23, 9:50 AM
Fuzzy logic was in favor to the relative new field of neural nets (ML as we know it today). Because from the limitations in industrial control neural nets were too costly. Yes, we had already things like the first i386embedded. But that was way too expansive. Most of the PLCs were based on 16-bit microcontrollers and even those were more on the expansive side.
So if you think commercially in terms of industrial control systems, then Fuzzy Logic was implemented and used. I assume old systems can be found anywhere even today. Remember that in industrial control 20 years is no time.
Today, everybody in industrial control is also about ML. But here again, that is still on the expansive side. Such Nvidia Jetsons do not come for cheap.
by wrp on 5/26/23, 9:20 AM
Among the more philosophically inclined, there was controversy other whether fuzzy logic was really something new or just a repackaging of old ideas with a misleading vocabulary. There was a book on how this debate played out in journal articles, but I can't think of the title now.
by vivegi on 5/26/23, 9:02 AM
by ly3xqhl8g9 on 5/26/23, 8:08 AM
† one speculative use would be to assign degrees(?) of agency, some kind of a future unit of measurement: a person can be 100% evolved, or maybe 49% evolved and 51% designed (prosthetics, neural implants, and so on).
[1] https://en.wikipedia.org/wiki/Characteristica_universalis
[2] 2017, Fuzzy Dual Numbers: Theory and Applications, https://dl.acm.org/doi/book/10.5555/3172931
[3] "Fuzzy Dual Dynamic Programming", https://hal-enac.archives-ouvertes.fr/hal-01372543/document
[4] "Dual Numbers & Automatic Differentiation", https://blog.demofox.org/2014/12/30/dual-numbers-automatic-d...
by tpoacher on 5/26/23, 12:20 PM
The first, is the true fuzzy logic, which consists of Fuzzy Set theory and Fuzzy Measure theory. The two are distinct but interrelated, and heavily mathematical in nature. In particular, Fuzzy Measure theory completely subsumes Probability theory (i.e. probability theory is a strict subset of fuzzy measure theory, and therefore anything that can be expressed in probabilistic terms can be expressed in equivalent fuzzy measure theory terms, with leeway for added generality).
The second, is a class of techniques, based on Fuzzy Set / Fuzzy Measure theory, which implement some very simple behaviours which allow you to solve a particular class of problems. This is the standard Sugeno/Mamdani logic, fuzzy inference as the application of a few straightforward rules for conducting conjunction/disjunction among fuzzy rules.
The first is still thriving, but largely in the theoretical realm.
The second is shunned, on the basis that it feels like a bunch of interpolation heuristics, and a Bayesian approach is usually "better", even if not as intuitive of straightforward.
Unfortunately this has had a self-prophetic impact, in the sense that any literature which would have been better described as fuzzy, is labelled as probabilistic, even if strictly speaking the concepts involved are not strictly-speaking probabilities, but should more correctly have been addressed as fuzzy measures.
Personally I have published papers where I had to "disguise" fuzziness as probability just to get it considered for publication, or where I had to "apologize" for the term, or making it explicit that fuzziness in this context is unrelated to this set of tools that is typically thought of as "the fuzzy approach".
Having said that, one area that is thriving is the use of Fuzzy methods in the context of more general AI / NeuralNetwork methods, particularly in the context of explainability. Type 2 Fuzzy methods in particular (which introduce one or more layers/dimensions of fuzziness/uncertainty over the membership function itself) are quite an active area of research.
Finally, there are some frameworks that borrow heavily from fuzzy theory, but strictly speaking are independent. The one that interested me the most in recent years was "Subjective Logic", proposed by a guy called Audun Jøsang; this sounded like a very interesting logic framework and I was very keen to use it in my work, but in the end I just didn't have the time to justify that investment ... It's worth looking up though. The basic idea is that you have a framework where you have rules that have a built-in way of carrying uncertainty with them, with a one-to-one mapping with beta probabilities over your fuzzy-like rules, and a set of logical operations (which map to the usual logical operators) that are adapted to carry the uncertainty with them. This means that you can reduce your problem into a set of simple logical statements, and the framework would then take care of the uncertainty calculations for you seamlessly.
by ndsipa_pomu on 5/26/23, 9:12 AM
by regularfry on 5/26/23, 8:33 AM
by re36081240 on 5/26/23, 12:15 PM
by born-jre on 5/26/23, 9:52 AM
by pestatije on 5/25/23, 9:44 AM
by revskill on 5/26/23, 7:42 AM