5 ms·

Rule-Based Expert Systems: The Mycin Experiments (1984)

92 points by mindcrime 1y ago

bane 1y ago
One of my first jobs was helping build an expert system for a, even today, complex computational linguistics problem. The company had a rich corporate library full of academic books on expert systems, decision trees, first gen (pre-winter) AI, and some early books on early ML approaches. I remember seeing this book in particular and its evocative title caused me to look deeper into the library than I would have normally. Our core system was built of thousands upon thousands of hand-crafted rules informed by careful statistical analysis of hundreds of millions of entries in a bulk data system. Part of my job was to build the system that analyzed the bulk data and produced the stats, and the other part was carefully testing and fixing the rulesets for certain languages. It was mind-numbing work, and looking back we were freakishly close to all the bit and pieces needed for then bleeding-edge ML had we chosen to go that way. However, we chose expert systems because it gave us tremendous insight into what was happening, and the opportunity to debug and test things at an incredibly granular scale. It was fully possible to say "the system has this behavior because of xyz" and it was fully possible to tune the system at individual character levels of finesse. Had we wanted to dive into ML, we could have used this foundation as a bootstrap into building a massive training set. But the founders biased towards expert systems and I think, at the time, it was the right choice. The technology was acquired, and I wonder if the current custodians use it for those obvious next-step purposes.
- nowittyusername 1y ago
  Your post is making me think maybe there is quite a lot of lost knowledge out there somewhere that maybe has pertinence in modern day agentic AI system building use. I am currently experimenting in building my own AI system that uses LLM's as the "engine" but the "harness" around the said LLM will do most of the heavy lifting. It will have internal verification systems, grounding information , metadata, etc... And I find myself making a lot of automated scripts as part of that process as I have a personal motto that its always better to automate everything possible with scripts first and only use LLM's as a last resort or for things you can script away. And that is making me look more and more in to old techniques that have been long established way back when...
  - ozim 1y ago
    Well from what I remember from university most of expert systems went into bust because they were promising what ML today promise. The maintenance of the rules or for you scripts for complex tasks is much more work than anyone is willing to commit to. Also big problem was finding out tacit knowledge and no one was able to code that reliably in. ML today is promising you won’t have to hand code the rules you just push data and system finds out what the rules are and then can handle new data. I don’t have to code the rules to check if there is a cat in the picture - that definitely works. Making rules on data that is not so often found on the internet that’s still going to be a hassle. Rules change and world change and for example knowledge cut off is I think still a problem. In the end yes you can build nice system for some use case where you plugin LLM for classification and you most likely will make money on it. This just won’t be „what was promised” so AGI and we are stuck with this promise and a lot of people won’t accept less than that.
  - arethuza 1y ago
    I wonder what would happen if you used an LLM to write the rules?
    - nowittyusername 1y ago
      That's exactly what I am focusing on. IMO getting rid of the biggest pain points regarding script building but also benefitting from modern day AI systems. "Have your cake and eat it too." We know that scripts are far more reliable then LLM's, but to build a good complex script is a pain in the ass and takes tremendous effort in creating and maintaining/debugging. So we use leverage modern day generative systems and have them be the builders and maintainers of said scripts. So the most intensive part for a human now comes down to creating a robust AI system that is able to build scripts reliable and then use them in conjunction with its own generative capabilities. Basically teach the machine to create the tools it will use and maintain, after that drop the human from the loop.
  - mindcrime 1y ago
    Your post is making me think maybe there is quite a lot of lost knowledge out there somewhere that maybe has pertinence in modern day agentic AI system building use. I agree with that. In fact, that mindset is what led me to this book in the first place. I was exploring an older book on OPS5[1] and saw this book mentioned, and started looking for it and found that it is freely available online. Seemed like something the HN crowd might enjoy, so here we are. And that is making me look more and more in to old techniques that have been long established way back when... I suspect that there is some meat on that bone. I'm exploring this particular area as well. I think there's some opportunity for hybridization between LLM's / GenAI and some of these older approaches. [1]: https://en.wikipedia.org/wiki/OPS5 https://en.wikipedia.org/wiki/OPS5
- ACCount37 1y ago
  Back when expert systems were relevant, we had nowhere near the amount of compute that's basically required for modern day ML. Even if the data somehow was in place for it.
andrehacker 1y ago
Ah, the early days of AI. If a book or movie is ever made about the history of AI, the script would include this period of AI history and would probably go something like this… (Some dramatic license here, sure. But not much more than your average "based on true events" script.) In 1957, Frank Rosenblatt built a physical neural network machine called the Perceptron. It used variable resistors and reconfigurable wiring to simulate brain-like learning. Each resistor had a motor to adjust weights, allowing the system to "learn" from input data. Hook it up to a fridge-sized video camera (20x20 resolution), train it overnight, and it could recognize objects. Pretty wild for the time. Rosenblatt was a showman—loud, charismatic, and convinced intelligent machines were just around the corner. Marvin Minsky, a jealous academic peer of Frank, was in favor of a different approach to AI: Expert Systems. He published a book (Perceptrons, 1969) which all but killed research into neural nets. Marvin pointed out that no neural net with a depth of one layer could solve the "XOR" problem. While the book's findings and mathematical proof were correct, they were based on incorrect assumptions (that the Perceptron only used one layer and that algorithms like backpropagation did not exist). As a result, a lot of academic AI funding was directed towards Expert Systems. The flagship of this was the MYCIN project. Essentially, it was a system to find the correct antibiotic based on the exact bacteria a patient was infected with. The system thus had knowledge about thousands and thousands of different diseases with their associated symptoms. At the time, many different antibiotics existed, and using the wrong one for a given disease could be fatal to the patient. When the system was finally ready for use... after six years (!), the pharmaceutical industry had developed “broad-spectrum antibiotics,” which did not require any of the detailed analysis MYCIN was developed for. The period of suppressing Neural Net research is now referred to as (one of) the winter(s) of AI. -------- As said, that is the fictional treatment. In reality, the facts, motivations, and behavior of the characters are a lot more nuanced.
- Animats 1y ago
  Not that wrong. I went through Stanford CS when those guys were in charge. It was starting to become clear that the emperor had no clothes, but most of the CS faculty was unwilling to admit it. It was really discouraging. Peak hype was in "The fifth generation: artificial intelligence and Japan's computer challenge to the world" (1983), by Feigenbaum. (Japan at one point in the 1980s had an AI program which attempted to build hardware to run Prolog fast.) Trying to use expert systems for medicine lent an appearance of importance to something that might work for auto repair manuals. It's mostly a mechanization of trouble-shooting charts. It's not totally useless, but you get out pretty much what you carefully put in.
anthk 1y ago
Common Lisp should have those too in the Paradigms of Artificial Intelligence programming book. Code: https://github.com/norvig/paip-lisp https://github.com/norvig/paip-lisp Book https://archive.org/details/github.com-norvig-paip-lisp_-_2020-10-18_18-45-27 https://archive.org/details/github.com-norvig-paip-lisp_-_20... As for the interpreter, SBCL works fine everywhere; if not, pick ECL and CCL.
cess11 1y ago
MYCIN was a high profile project, but rule engines in this vein come in handy quite often. With some basic Prolog knowledge and a decent implementation pretty much any set of data files can quickly be turned into an ad hoc 'expert system' lite. The simplest example and the one I usually bring up is log files, where the primary delimiter is \n and the secondary is likely some whitespace, which can easily be replaced with Prolog delimiters and a bit of decoration. This turns the data into Prolog code which can be consulted as is and complemented with rules abstracting complex queries. Something similar can be done with JSON files. https://www.scryer.pl/ https://www.scryer.pl/ https://www.metalevel.at/prolog/dcg https://www.metalevel.at/prolog/dcg https://www.metalevel.at/prolog/expertsystems https://www.metalevel.at/prolog/expertsystems

92 points by mindcrime 1y ago

bane 1y ago

One of my first jobs was helping build an expert system for a, even today, complex computational linguistics problem. The company had a rich corporate library full of academic books on expert systems, decision trees, first gen (pre-winter) AI, and some early books on early ML approaches. I remember seeing this book in particular and its evocative title caused me to look deeper into the library than I would have normally. Our core system was built of thousands upon thousands of hand-crafted rules informed by careful statistical analysis of hundreds of millions of entries in a bulk data system. Part of my job was to build the system that analyzed the bulk data and produced the stats, and the other part was carefully testing and fixing the rulesets for certain languages. It was mind-numbing work, and looking back we were freakishly close to all the bit and pieces needed for then bleeding-edge ML had we chosen to go that way. However, we chose expert systems because it gave us tremendous insight into what was happening, and the opportunity to debug and test things at an incredibly granular scale. It was fully possible to say "the system has this behavior because of xyz" and it was fully possible to tune the system at individual character levels of finesse. Had we wanted to dive into ML, we could have used this foundation as a bootstrap into building a massive training set. But the founders biased towards expert systems and I think, at the time, it was the right choice. The technology was acquired, and I wonder if the current custodians use it for those obvious next-step purposes.

nowittyusername 1y ago
Your post is making me think maybe there is quite a lot of lost knowledge out there somewhere that maybe has pertinence in modern day agentic AI system building use. I am currently experimenting in building my own AI system that uses LLM's as the "engine" but the "harness" around the said LLM will do most of the heavy lifting. It will have internal verification systems, grounding information , metadata, etc... And I find myself making a lot of automated scripts as part of that process as I have a personal motto that its always better to automate everything possible with scripts first and only use LLM's as a last resort or for things you can script away. And that is making me look more and more in to old techniques that have been long established way back when...
- ozim 1y ago
  Well from what I remember from university most of expert systems went into bust because they were promising what ML today promise. The maintenance of the rules or for you scripts for complex tasks is much more work than anyone is willing to commit to. Also big problem was finding out tacit knowledge and no one was able to code that reliably in. ML today is promising you won’t have to hand code the rules you just push data and system finds out what the rules are and then can handle new data. I don’t have to code the rules to check if there is a cat in the picture - that definitely works. Making rules on data that is not so often found on the internet that’s still going to be a hassle. Rules change and world change and for example knowledge cut off is I think still a problem. In the end yes you can build nice system for some use case where you plugin LLM for classification and you most likely will make money on it. This just won’t be „what was promised” so AGI and we are stuck with this promise and a lot of people won’t accept less than that.
- arethuza 1y ago
  I wonder what would happen if you used an LLM to write the rules?
  - nowittyusername 1y ago
    That's exactly what I am focusing on. IMO getting rid of the biggest pain points regarding script building but also benefitting from modern day AI systems. "Have your cake and eat it too." We know that scripts are far more reliable then LLM's, but to build a good complex script is a pain in the ass and takes tremendous effort in creating and maintaining/debugging. So we use leverage modern day generative systems and have them be the builders and maintainers of said scripts. So the most intensive part for a human now comes down to creating a robust AI system that is able to build scripts reliable and then use them in conjunction with its own generative capabilities. Basically teach the machine to create the tools it will use and maintain, after that drop the human from the loop.
- mindcrime 1y ago
  Your post is making me think maybe there is quite a lot of lost knowledge out there somewhere that maybe has pertinence in modern day agentic AI system building use. I agree with that. In fact, that mindset is what led me to this book in the first place. I was exploring an older book on OPS5[1] and saw this book mentioned, and started looking for it and found that it is freely available online. Seemed like something the HN crowd might enjoy, so here we are. And that is making me look more and more in to old techniques that have been long established way back when... I suspect that there is some meat on that bone. I'm exploring this particular area as well. I think there's some opportunity for hybridization between LLM's / GenAI and some of these older approaches. [1]: https://en.wikipedia.org/wiki/OPS5 https://en.wikipedia.org/wiki/OPS5
ACCount37 1y ago
Back when expert systems were relevant, we had nowhere near the amount of compute that's basically required for modern day ML. Even if the data somehow was in place for it.

andrehacker 1y ago

Ah, the early days of AI. If a book or movie is ever made about the history of AI, the script would include this period of AI history and would probably go something like this… (Some dramatic license here, sure. But not much more than your average "based on true events" script.) In 1957, Frank Rosenblatt built a physical neural network machine called the Perceptron. It used variable resistors and reconfigurable wiring to simulate brain-like learning. Each resistor had a motor to adjust weights, allowing the system to "learn" from input data. Hook it up to a fridge-sized video camera (20x20 resolution), train it overnight, and it could recognize objects. Pretty wild for the time. Rosenblatt was a showman—loud, charismatic, and convinced intelligent machines were just around the corner. Marvin Minsky, a jealous academic peer of Frank, was in favor of a different approach to AI: Expert Systems. He published a book (Perceptrons, 1969) which all but killed research into neural nets. Marvin pointed out that no neural net with a depth of one layer could solve the "XOR" problem. While the book's findings and mathematical proof were correct, they were based on incorrect assumptions (that the Perceptron only used one layer and that algorithms like backpropagation did not exist). As a result, a lot of academic AI funding was directed towards Expert Systems. The flagship of this was the MYCIN project. Essentially, it was a system to find the correct antibiotic based on the exact bacteria a patient was infected with. The system thus had knowledge about thousands and thousands of different diseases with their associated symptoms. At the time, many different antibiotics existed, and using the wrong one for a given disease could be fatal to the patient. When the system was finally ready for use... after six years (!), the pharmaceutical industry had developed “broad-spectrum antibiotics,” which did not require any of the detailed analysis MYCIN was developed for. The period of suppressing Neural Net research is now referred to as (one of) the winter(s) of AI. -------- As said, that is the fictional treatment. In reality, the facts, motivations, and behavior of the characters are a lot more nuanced.

Animats 1y ago
Not that wrong. I went through Stanford CS when those guys were in charge. It was starting to become clear that the emperor had no clothes, but most of the CS faculty was unwilling to admit it. It was really discouraging. Peak hype was in "The fifth generation: artificial intelligence and Japan's computer challenge to the world" (1983), by Feigenbaum. (Japan at one point in the 1980s had an AI program which attempted to build hardware to run Prolog fast.) Trying to use expert systems for medicine lent an appearance of importance to something that might work for auto repair manuals. It's mostly a mechanization of trouble-shooting charts. It's not totally useless, but you get out pretty much what you carefully put in.

anthk 1y ago

Common Lisp should have those too in the Paradigms of Artificial Intelligence programming book. Code: https://github.com/norvig/paip-lisp https://github.com/norvig/paip-lisp Book https://archive.org/details/github.com-norvig-paip-lisp_-_2020-10-18_18-45-27 https://archive.org/details/github.com-norvig-paip-lisp_-_20... As for the interpreter, SBCL works fine everywhere; if not, pick ECL and CCL.

cess11 1y ago

MYCIN was a high profile project, but rule engines in this vein come in handy quite often. With some basic Prolog knowledge and a decent implementation pretty much any set of data files can quickly be turned into an ad hoc 'expert system' lite. The simplest example and the one I usually bring up is log files, where the primary delimiter is \n and the secondary is likely some whitespace, which can easily be replaced with Prolog delimiters and a bit of decoration. This turns the data into Prolog code which can be consulted as is and complemented with rules abstracting complex queries. Something similar can be done with JSON files. https://www.scryer.pl/ https://www.scryer.pl/ https://www.metalevel.at/prolog/dcg https://www.metalevel.at/prolog/dcg https://www.metalevel.at/prolog/expertsystems https://www.metalevel.at/prolog/expertsystems