I put in a little disclaimer for emphasis 

 *Just to emphasize – the coverage below is uneven and only intended to 
give a general feel and some links for consideration, and not a critical 
overview in any way. *
On Friday, February 11, 2022 at 9:26:39 PM UTC-8 Mike Archbold wrote:

> Thanks... I will update the notes. The notes are VERY general, just to get 
> a "feel" was my intent, not a criticism or drawing comparisons.... the 
> coverage is uneven. So, I say it's "notes" and it's not proper research... 
> I bolded ACT-R only for emphasis on what sounds like a key feature of the 
> design, not even related to the time. I don't know much ACT-R, which is why 
> I stuck a long description in there.  It sounds like you've done a lot of 
> great work on OpenCog.
>
> On Friday, February 11, 2022 at 7:48:38 PM UTC-8 linas wrote:
>
>> And one final hopefully short comment:
>>
>> > NARS, SOAR, ACT-R
>>
>> I want to draw a few more distinctions.  First, "classic" OpenCog is 
>> (was?) a theory of mind or a theory of cognition (a "cognitive model"?), 
>> having more than a few similarities to the above systems.  This "classic" 
>> OpenCog is described in several books by Goertzel et al, and assorted 
>> papers, conference proceedings, etc. Assorted variants of it were built.
>>
>> All of these incarnations of OpenCog were built on a generic 
>> infrastructure, the "Atomspace". The AtomSpace is meant to provide an 
>> "easy-to-use" base on which different cognitive theories can be created, 
>> explored, developed.  It tries to be impartial, providing a collection of 
>> tinker-toy parts which you can assemble yourself, or extend, implement, 
>> re-implement as needed to pursue any one particular theory or vision of 
>> what cognition is. 
>>
>> Because we've turned the crank on this 3 or 4 or 5 times, the lower 
>> layers have gotten fairly generic, and are debugged, stable, 
>> performance-optimized and can support the weight of more complex devices to 
>> be built on top of them. The exploration of higher layers continues 
>> unabated.  Most of what you abstracted about NARS, SOAR, ACT-R would count 
>> as "higher layers". 
>>
>> To rephrase: the AtomSpace allows you to "roll your own" temporal logic. 
>> I don't care- have at it, use your favorite theory. You mention ACT-R as 
>> having declarative, and procedural memory, and ACT-R being a production 
>> system. Sure, we can do all three styles in the AtomSpace, simultaneously, 
>> on the same data. I don't care: do it however you want. You bolded: At 
>> each moment, an internal pattern matcher [in ACT-R] searches for a 
>> production that matches the current state of the buffers. Only one such 
>> production can be executed at a given moment By contrast, in the 
>> AtomSpace, you can run productions one at a time, or in parallel, or 
>> distributed across the network. Don't care. Or, instead of productions, you 
>> can use term-rewriting, graph rewriting, don't care. The toolset is there. 
>>
>> -- Linas
>>
>> On Fri, Feb 11, 2022 at 7:50 PM Linas Vepstas <[email protected]> 
>> wrote:
>>
>>> Hi Mike,
>>>
>>> > looking like CLIPS a bit to me.
>>>
>>>
>>> And not by accident. There are, however, some deep and fundamental 
>>> differences. These are:
>>>
>>>
>>> * The "rules" are kept in a graph database that can be saved to disk in 
>>> several formats, saved to SQL, no-SQL, and transmitted by network to other 
>>> network nodes.
>>>
>>>
>>> * The graph store is more generic than just "rules", you can store 
>>> anything you want in it. It's a generalized KR system. If you don't like 
>>> the default KR style, you can invent your own: all knowledge graphs are not 
>>> just static graphs, but are also executable, and you get to pick how that's 
>>> done. (OK, so if you invent your own, it might not work so well with PLN, 
>>> and whatever temporal subsystem gets created. So compatibility is your 
>>> responsibility, too.)
>>>
>>>
>>> * Unlike CLIPS (or Prolog) rules/expressions can have more than just 
>>> true/false values. They can be given floating-point valuations, for 
>>> example, Bayesian probabilities or fuzzy-logic percentages. They can be 
>>> given vector-of-floats, e.g. two numbers: probability & confidence. Or a 
>>> vector of 653 floats, from some neural net. Or a vector of strings. Or a 
>>> nested tree of floats and strings. Or whatever. Each valuation is a generic 
>>> key-value DB. And not just only "true/false".
>>>
>>>
>>> The default PLN rules that are CLIPS-like use a blend of probability 
>>> theory and fuzzy logic. But again, you don't have to use these, you can 
>>> invent your own.
>>>
>>>
>>> -- Linas
>>>
>>>
>>>
>>> On Fri, Feb 11, 2022 at 6:02 PM Mike Archbold <[email protected]> wrote:
>>>
>>>> Thanks everybody for your comments. There is a time philosophy meetup 
>>>> event this Sunday, and I put together some very general time notes I 
>>>> cobbled together:  
>>>> https://docs.google.com/document/d/1_PLknbLKL7ZGEupy6tBQR-J5rkFgt3s-dOHKn4LZKIU/edit?usp=sharing
>>>> Please let me know if further comments. I appreciate your help!
>>>> Mike Archbold
>>>>
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>>>> .
>>>>
>>>
>>>
>>> -- 
>>> Patrick: Are they laughing at us?
>>> Sponge Bob: No, Patrick, they are laughing next to us.
>>>  
>>>
>>>
>>
>> -- 
>> Patrick: Are they laughing at us?
>> Sponge Bob: No, Patrick, they are laughing next to us.
>>  
>>
>>

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