Re: Vector embeddings for production rules

Sounds good, Dave. I will send you an invite to chat in February. And
thanks very much for the additional references and explanation. Very
excited if I can help.

Concerning the typo, on line 82, you wrote: "designed for used in
human-like AI applications". It should be: "designed for use in human-like
AI applications". I.e., "used" --> "use".

Thanks very much,
Matteo

On Fri, 22 Dec 2023 at 09:59, Dave Raggett <dsr@w3.org> wrote:

> Hi Matteo,
>
> Send me details for the typo in the README and I will fix it.
>
> Happy to speak to you in the new year.   I am pretty busy in January, so
> sometime in February would be better.
>
> In respect to the use of vector embeddings for production rules, I’ve
> found a few references which look very relevant.
>
>    -
>
>    Embedding logical queries on knowledge graphs
>    <https://proceedings.neurips.cc/paper_files/paper/2018/file/ef50c335cca9f340bde656363ebd02fd-Paper.pdf>,
>    2018, Hamilton et al.
>
>    *Logical queries and edge predictions on graph embeddings in low
>    dimension spaces. Conjunctive queries are mapped to embeddings and used for
>    approximate nearest neighbour search against the graph.*
>
>
>    -
>
>    Knowledge Graph Embedding: A Survey from the Perspective of
>    Representation Spaces <https://arxiv.org/pdf/2211.03536.pdf>, 2023,
>    Jiahang Dao et al. *We explore the advantages of mathematical spaces
>    in different scenarios and the reasons behind them.*
>
>
>    -
>
>    Approximate nearest neighbors: towards removing the curse of
>    dimensionality
>    <https://www.theoryofcomputing.org/articles/v008a014/v008a014.pdf>,
>    1998, Indyk and Motwani.
>
> The means to compute embeddings for conjunctive queries is just what is
> needed for indexing simple production rules, followed by stochastic
> selection of matching rules using approximate nearest neighbour search. The
> survey of knowledge graph embeddings also looks good in respect to use in
> episodic and encyclopaedic memory, e.g. using different embeddings for
> entities and relations.
>
> In a cognitive agent we would like to balance intuition and deliberation,
> for this, it is useful to consider the distinction between implicit memory
> and explicit memory.  The former supports intuition and everyday skills,
> and is the basis for today’s generative AI, based upon gradient descent and
> training against large datasets. The latter covers the things you remember
> individually, e.g. your last birthday. Explicit memory can be likened to a
> database with read, write and update operations.
>
> The implementation of explicit memory should support machine learning,
> e.g. induction of generalisations and specialisations across instances,
> including entities and relations. For plausible reasoning we also need soft
> metadata as a basis for inferences.  Explicit memory should further mimic
> human memory in respect to the forgetting curve, spacing effect and
> spreading activation, as these are important in respect to recalling what
> is important and ignoring what is not.
>
> This points to plenty of opportunities for experimentation.
>
> Best regards,
>     Dave
>
> On 22 Dec 2023, at 13:45, Matteo Bianchetti <mttbnchtt@gmail.com> wrote:
>
> 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>
>>
>>
>>
>>
> Dave Raggett <dsr@w3.org>
>
>
>
>