Re: unboxed structure fields (or class slots)

> On Jan 13, 2026, at 12:48 PM, David McClain (as dbm at refined-audiometrics dot com) <[email protected]> wrote:
> 
> As I recall, from first encounters with Harlequin Common Lisp, the early AI used “reasoning systems” - forward chaining and backward chaining. HCL offered their add-on Prolog system as well as a forward chaining system. Perceptrons didn’t participate in that realm.

Yes, I believe the current LispWorks “KnowledgeWorks” product is exactly that. I’ve used it a bit for some projects (given it’s a lot better implemented and supported than my old university code :-), and it’s not a bad system, though I don’t have an enterprise license to use it anymore. Back in the day, there were several commercial expert systems as well and there was a PROLOG for the LispMs too. In fact the Japanese 5th Generation project was centered around building a “PROLOG Machine” though that was a failure as unification introduced too many synchronization points to allow anything but single-thread backtracking.

I wrote a few reasoning systems myself during my university days, all forward and backward chaining with, e.g., type inference, though these were applied to NLU, not expert systems, but could be considered PROLOG variants (basically, take PROLOG, add a bunch of features to make semantic parsing and pragmatics easier). We also used the system for doing plan recognition, some degree of commonsense reasoning, etc. Early plan recognition was intractable trying to solve with logic, but we later started using other techniques using semantic nets and Dempster-Shafer type approaches to the math. These were all pointed toward expressiveness, not efficiency so wouldn’t be of much interest today. (Higher order logic, why sure! :-)

Nothing specifically numeric. SNEPS out of SUNY Buffalo had some interesting quantifier support to deal with things like “at least three” IIRC but we didn’t go that route. 

> 
> We used Perceptrons starting in the early 90’s for building classifiers and recognizers. And then Kolmogorov showed that a simple feedback PN could be used to model chaotic attractor basins of dimensions equal to the number of input legs. Atmospheric turbulence has a fractal dimension around 7 and that led us to train a multilayer PN’s one long weekend on speckle images, using a DG Eagle, to train a “rubber mirror” for removal of atmospheric scintillation of star images.

We used to distinguish between “adaptation” and “learning” the latter required inducing actual rules (which could be inspected and hand modified if needed), while the former was mostly to deal with sensor systems and classification. I’m pretty sure our students that were doing that were using basic NNs not Perceptrons though (Perceptrons have no hidden layers).

> 
> We also used Probabilistic Neural Networks to build target trackers for search and destroy missions - LIDAR underwater mine detection systems, and EKV Kinetic Kill Vehicles for taking out incoming nuclear warheads.

I did some work with HRL a dozen or so years back to use first-order logic functions to represent probabilities (e.g. confidence), so you could do probabilistic (adversarial) planning without actually having to deal with the math, after giving up on using MDPs. More qualitative than quantitative. The more interesting part of the research dealt with autoepistemic logic, so the system could reason about what it didn’t know and then create plans to find out to reduce uncertainty.

> 
> Early Kohonen (highly interconnected mesh) networks were used for storing knowledge domains. 
> 
> LLMs would be another 20-30 years later…
> 


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