Re: Vector embeddings for production rules

Hi Dave,

Thanks so much for the email. I took some time to read through the
references and study part of the github repository. I would be very happy
to help with the project that you describe.

On another matter, I tried to fix a typo in the README and push a new
version in a branch, but got an error message (permission denied). Maybe we
can talk about this too whenever you are free. I am happy to meet almost
any time (I am in the ET time zone).

Thanks very much,
Matteo

On Mon, 18 Dec 2023 at 10:44, Dave Raggett <dsr@w3.org> wrote:

> I’ve been studying ideas for implementing Type 1 & 2 cognition based upon
> vector embeddings for production rules. This is inspired by the role of
> production rules in symbolic cognitive architectures such as ACT-R and
> SOAR, as well as this community group's work on chunks & rules.
>
> Some key papers include:
>
> *1) Neural machine translation by jointly learning to align and translate*,
> 2015, Bahhanau, Cho and Bengio, see: https://arxiv.org/abs/1409.047
>
> *2) Attention is all you need*, 2017, Ashish Vaswani et al., see:
> https://arxiv.org/abs/1706.03762
>
> *3) Neural Production Systems*, March 2022, Aniket Didolkar et al, see:
> https://arxiv.org/pdf/2103.01937.pdf
>
> A production rule system determines which rules match the current state of
> working memory, stochastically selects the best matching rule, and applies
> it to update working memory. Rules include variables as a basis for
> generalisation. An artificial neural network can be designed to learn rules
> through reinforcement learning.
>
> The first reference above describes how English can be translated to
> French using a mechanism to determine the soft-alignment of the current
> word with the preceding and following words. The second reference
> introduces Transformers as model of self-attention that can be executed in
> parallel, and forms the basis for today’s large language models (e.g.
> ChatGPT) which statistically predict text continuations to a user supplied
> prompt. The third reference extends these ideas to show how attention
> supports the process of matching rule conditions to working memory.
>
> I am hoping to apply aspects of all three papers in new work on applying
> production rules to Type 2 cognition, i.e. sequential deliberative
> cognitive steps as a basis for reasoning. This can be thought of as
> reimplementing chunks & rules in neural networks. This will exploit
> feed-backward connections for retained state in combination with the
> feed-forward connections found in existing language models. I am looking
> forward to implementing experimental versions of these ideas in PyTorch.
>
> Any offers of help would of course be very welcome!
>
> p.s. this is part of a roadmap for work including natural language
> processing and continual learning based upon integrating episodic and
> encyclopaedic memory.
>
> Best regards,
>
> Dave Raggett <dsr@w3.org>
>
>
>
>