Concurrent and Distributed NARS

[djire...@gmail.com]

Hi everybody,

I've been experimenting with NARS running on the BEAM virtual machine and using the Erlang's Open Telecom Platform (OTP). 

This  gives concurrency and distributed computing (along with fault tolerance) out-of-the-box (of course not without it's own problems). 

I was thinking about writing it up for the next AGI Conf 2026, but wondered if there were any other attempts at a concurrent / distributed NARS I could refer to. I recall there were at least 50 implementations (from a slide presented by someone else) but couldn't find any distributed implementations nor were they mentions in this google group. 

I was considering to heavily cite  "The Event Buffer of OpenNARS" paper which had a parallel architecture to it. 

Appreciate any thoughts. 

Regards,

David

 

 

[Pei Wang]

Hi David,

The relevant works I can think of at the moment:

• Intelligent Multiagent Systems based on Distributed Non-Axiomatic Reasoning
• https://github.com/rasom/opennars2

There have been discussions and experiments on multi-agent cooperation and hardware acceleration for OpenNARS and ONA; however, I have not found any publicly available writing or code.

Looking forward to your results!

Regards,

Pei

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[David Ireland]

Thanks Pei.  I hadn't seen Dejan Mitrovic's thesis. 

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[Patrick Hammer]

Hi David!

The most advanced distributed NARS implementation so far is Tony Lofthouse's ALANN2018, which scales NARS (NAL-1 to NAL-6) to thousands of threads operating concurrently on shared concept memory with high efficiency: https://github.com/opennars/ALANN2018

However, temporal and procedural reasoning are much harder to handle in a distributed architecture, and ALANN2018 did not address them. More generally, these aspects have also long been the weakest parts of NARS conceptually, not just implementation-wise, with many missing and underspecified ideas, making embodied NARS-based divert far from AGI expectations.

I recently tried to push beyond ONA's sensorimotor capabilities, with some success, focusing on improved temporal and event-stream reasoning but with NAL1-8 support: https://github.com/patham9/estream

While limited, NAL-based sensorimotor can still be useful though, as it can allow agents to adapt very effectively when event encodings are at the right abstraction level.

Best regards,

Patrick

[David Ireland]

Hi Patrick,

Thanks! I've been meaning to read Tony's book. 

Am I right in assuming the temporal issues in a distributed cluster are because it would be incredibly difficult to synchronise a time stamp? 

Are you also able to elaborate on some of the other weaker aspects?

Thanks.

Regards,

David

 

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[Patrick Hammer]

Hi David!

I just saw that you responded, sorry for the delay.

This is mainly a coordination problem. To detect temporal patterns, large numbers of events must be combined during reasoning. In a distributed design where events are routed to separate concepts, this quickly breaks down: coordination becomes difficult, and some form of centralized structure (buffers, FIFOs, etc.) becomes necessary.

Independent of AIKR, the scale of event integration truly matters. At high well-prepared/engineered abstraction levels, relations like <a =/> b> with single events can be useful. But for high-throughput, sensory-like streams, even sequences or parallel conjunctions of ~20 events are often insufficient. Dependencies between events are often way more complex than simply "sequence of events A predicts B", and behavior demands extremely fine-grained, tightly coordinated action, something that is undermined both by distributed system realities and by the constraints of logical descriptions.

Best regards,

Patrick

[djire...@gmail.com]

Hi all interested, 

With regards to the concurrent / distributed NARS (developed for the BEAM VM i.e. Erlang)  I mentioned in the previous post  - I've been experimenting with distributed  & isolated  concepts (that have standard bag data structures) that are able to communicate to other concepts to share tasks/ beliefs etc.  One of the major bottlenecks presenting is the multiple variable introduction rule taking up most of the messaging bandwidth  - the speed up is substantial however, if I turn it off. 

It's a rule that matches a wide range of premises (hence the problem) but I can see it's practical uses  - On a side note I've been wondering if it also supports a rudimentary NAL-7 layer.

I've noticed OpenNARS for Applications has commented out these rules in the source code and wondered why that is and thought I'd ask.  

I'd appreciate any further thoughts and suggestions as I'm deciding whether to try to hack a solution or go back to the "drawing board". 

Thanks.

Regards,

David