Re: PM-KR Mission Updated — Procedural vs Declarative, Context Handling, Concrete Applications

The subject changed in Daniel's reply so I will continue on this thread.

The declarative boundaries approach to build bodies must be extended with more nuance to actually capture reality.

Reality is not uniform, containing paradoxes, and non-logical choices of value, and ignorance, among other "ripples" that do not fit into a strict model.

There are dimensions that have been missing from existing modeling approaches as they are difficult to reconcile in traditional systems.

These dimensions apply in a kind of fractal manner enabling effects in dependent layers. I have no idea how to go about defining this but again, these are my intuitions.

1. *Hard boundaries* - Boundaries that cannot be crossed / must not be crossed.

2. *Soft boundaries* - Boundaries that can be crossed knowingly with a consequent affect

3. *Blurred boundaries* - Boundaries that are poorly defined / understood / seen leading to confusion / deception / misguidance

4. *Broken boundaries* - Boundaries that are knowingly being violated without action to remedy

Boundaries are needed to structure a coherent system / body and there are different kinds of boundaries with different properties.

Structure embodies the intent and understanding of the author.

The resulting system structures reality and thus quality of life of users.

Structural and functional transparency is the safety net for authors of models/systems in a future where system creating individuals are held accountable for the harm they cause to others.

Christoph


On Sat, Feb 28, 2026, at 10:18 AM, Christoph wrote:
> 
> On Sat, Feb 28, 2026, at 7:51 AM, Milton Ponson wrote:
>> In the AIKR CG I repeatedly mentioned being personally busy fleshing out mandala graph theory. What I actually did not mention is that it addresses both declarative and procedural approaches.
>> 
>> I now realize that mathematicians and computer scientists when addressing foundational principles of artificial intelligence tend to speak slightly different languages, and consequently I did not even use the computer speak terms declarative and procedural in my notes.
>> 
>> So I checked if I maybe right after all and this is what I found:
>> what is the difference and what are the key benefits of the declarative and procedural approach in knowledge representation for artificial intelligence
>> 
>> https://share.google/aimode/pL2tANlD6ee0Cp2uz
>> 
>> It seems I have been addressing doing both at the same time mathematically,  so maybe we should rephrase the mission statement to reflect the fact that we are trying to optimize the procedural approach, given the appropriate declarative approach as a starting point. This also resonates with the HP calculator and *dead brain mode* analogy given by Daniel.
> 
> This resonates with my work as well:
>  1. Declarative first to structure bodies through boundaries.
>  2. Procedural second to animate activity in and around bodies.
> A body is a whole system of something. All dimensions are considered. The structure is what binds concepts across dimensions.
> 
> These are intuitions I have been getting.
> 
> 
>> And it would the also reflect the way the human brain works where the distinction between the two is blurred as I also mentioned in a post in another Community Group.
>> Sometimes I wonder if a Wiki page combining elements, findings, discussions of multiple Community Groups could help. We are already cross-posting all the time. 
>> Would be an excellent idea for the PM-KR community group.
>> 
>> As a mathematician I would also like to point out the usefulness of creating weekly summary reports of reviews of new literature of interest, points brought up in posts, and links to work in other community groups, based on interaction and reflections triggered by cross-posts.
>> And these in terms could be cross-posted.
>> And these should be written in a condensed summary form, without all the verbal add-ons that AI chatbots typically generate.
> 
> This is an excellent idea!
> 
> I was already thinking I need to build something like that for myself to efficiently keep up.
> 
> How could we go about doing this practically? Do we need to build something for it or are there existing solutions?
> 
> Christoph
> 
> 
>> 
>> Milton Ponson
>> Rainbow Warriors Core Foundation
>> CIAMSD Institute-ICT4D Program
>> +2977459312
>> PO Box 1154, Oranjestad
>> Aruba, Dutch Caribbean
>> 
>> On Sat, Feb 28, 2026, 08:04 Daniel Ramos <capitain_jack@yahoo.com> wrote:
>>> __
>>> Dear PM-KR Community,
>>> 
>>> Following feedback from W3C veteran **Dave Raggett** (dsr@w3.org), I've significantly revised the PM-KR Community Group mission statement to address critical questions about our approach.
>>> 
>>> **Updated Mission:** https://www.w3.org/community/pm-kr/procedural-memory-knowledge-representation-pm-kr-community-group/
>>> 
>>>  ## What Changed (Summary)
>>> 
>>> ### 1. **Why Procedural Over Declarative?** (New Section)
>>> 
>>> **Dave's question:** "What's wrong with a declarative approach?"
>>> 
>>> We've added a detailed comparison showing:
>>> 
>>> | **Approach** | **Execution** | **Transparency** | **Composability** | **Context Handling** |
>>> |--------------|---------------|------------------|-------------------|----------------------|
>>> | **Declarative (RDF/OWL)** | ❌ No (describes, doesn't execute) | ✅ Transparent | ⚠️ Limited (static relationships) | ⚠️ Manual modeling |
>>> | **Neural Networks** | ✅ Yes (procedural) | ❌ Opaque (black box) | ❌ No (monolithic) | ✅ Learned (but unexplainable) |
>>> | **PM-KR** | **✅ Yes (procedural)** | **✅ Transparent** | **✅ Yes (compositional)** | **✅ Explicit context rules** |
>>> 
>>> **Key insight:** PM-KR combines **transparency of declarative systems** with **executability of neural networks** while adding **composability** that neither provides.
>>> 
>>> **Example:** Mathematical symbol "∫" (integral)
>>> - **Declarative (RDF):** `rdfs:label "integral" ; math:relatedTo :summation` ❌ Doesn't tell you HOW to compute or render
>>> - **PM-KR:** Provides `visual_rpn` (glyph rendering), `execution_rpn` (integration algorithm), `audio_rpn` (pronunciation), context-specific rules ✅
>>> 
>>> **Positioning:** PM-KR is the **execution layer** that complements declarative standards (RDF/OWL/JSON-LD), not replacing them.
>>> 
>>> ### 2. **Handling Context-Dependent Meanings** (New Section)
>>> 
>>> **Dave's question:** "What about subtly different meanings in different contexts?"
>>> 
>>> **PM-KR's answer:** Explicit context rules (procedural programs)
>>> 
>>> **Example: "Chair" in Different Contexts**
>>> - **Furniture catalog:** Photorealistic rendering + price metadata
>>> - **Architectural BIM:** Collision mesh + load-bearing physics
>>> - **Game environment:** Low-poly mesh + interaction rules (sittable, throwable)
>>> - **Accessibility:** Audio pronunciation + 3D-printable tactile mesh
>>> 
>>> **Result:**
>>> - Same base knowledge (`chair = seat + back + legs`)
>>> - Context-specific execution (inspectable procedural rules)
>>> - Composable (contexts inherit/override base procedures)
>>> - Transparent (not hidden in neural weights)
>>> 
>>> See revised mission for full JSON-LD example.
>>> 
>>> ### 3. **Concrete Applications** (New Section)
>>> 
>>> **Dave's recommendation:** "Explain PM-KR in respect to why it is needed and what applications it targets."
>>> 
>>> **Added 5 detailed examples:**
>>> 
>>> 1. **Education (MIT OpenCourseWare)**
>>>    - ONE procedural calculus textbook → AI tutors AND students consume same source
>>>    - Impact: Accessibility (visual, audio, tactile), AI integration, maintenance
>>> 
>>> 2. **Gaming (D&D SRD)**
>>>    - Game rules as procedural programs → Human DMs read, AI DMs execute (same source)
>>>    - Impact: Zero divergence, AI DM uses canonical rules (no hallucination)
>>> 
>>> 3. **Science (Nature Protocols)**
>>>    - Experimental protocols as procedural programs → Scientists read, lab robots execute
>>>    - Impact: Reproducibility crisis addressed (protocol description = execution)
>>> 
>>> 4. **Accessibility (Multi-Modal Textbooks)**
>>>    - Visual equations, spoken descriptions, tactile 3D-printed graphs (all from ONE source)
>>>    - Impact: Blind students access same knowledge as sighted (not separate "accessible version")
>>> 
>>> 5. **AI Training (Wikipedia Procedural KB)**
>>>    - Wikipedia as PM-KR source → AI queries during inference (no training duplication)
>>>    - Impact: Carbon reduction (train once, reference forever)
>>> 
>>> Each example shows **why procedural execution is needed** (not just declarative description) and **what problem it solves**.
>>> 
>>> ## Community Discussion Invited
>>> 
>>> **Questions for the group:**
>>> 
>>> 1. **Declarative integration:** How should PM-KR's execution layer integrate with existing RDF/OWL ontologies? Should we define mapping rules?
>>> 
>>> 2. **Context rule semantics:** Are the proposed context inheritance/override rules sufficient? What edge cases are we missing?
>>> 
>>> 3. **Application priorities:** Which of the 5 applications (education, gaming, science, accessibility, AI training) should we prioritize for early validation?
>>> 
>>> 4. **Neural network comparison:** Is our positioning vs neural networks (procedural + transparent vs procedural + opaque) clear? How can we strengthen this distinction?
>>> 
>>> 5. **PKN relationship:** Dave mentioned PKN (Procedural Knowledge Networks) as combining declarative approaches with qualitative metadata. Should we establish liaison with PKN researchers?
>>> 
>>> **Please share:**
>>> - Critical feedback (what's unclear, what's wrong?)
>>> - Additional use cases from your domain
>>> - Alternative approaches we should consider
>>> - Relevant prior art (papers, systems, standards)
>>> 
>>> ## Acknowledgment
>>> 
>>> Thank you, **Dave Raggett**, for the rigorous feedback. Your questions transformed our mission statement from "here's what we're doing" to "here's WHY procedural over declarative, HOW we handle context, and WHAT applications need this."
>>> 
>>> This is exactly the rigor PM-KR needs to mature into a viable W3C standard.
>>> 
>>> **To all members:** Please continue challenging our assumptions. PM-KR's strength will come from community scrutiny and diverse perspectives.
>>> 
>>> ## Current Status (Week 1 Complete)
>>> 
>>> **Momentum (Feb 20-28, 2026):**
>>> - ✅ 18+ members (MIT, Huawei, JSON-LD co-creator Manu Sporny)
>>> - ✅ Mission statement revised based on community feedback (v1.0 → v1.1)
>>> - ✅ Initial specification drafts in GitHub
>>> - ✅ Reference implementation (Knowledge3D: Python/CUDA)
>>> - ✅ Empirical validation (50-90% compression, dual-client contract working)
>>> 
>>> **Next steps:**
>>> - **Q2 2026:** PM-KR Core Specification v0.5 (draft for community review)
>>> - **Q3 2026:** W3C TPAC breakout session
>>> - **Q4 2026:** PM-KR Core Specification v1.0
>>> 
>>> ## Resources
>>> 
>>> **Updated Mission Statement:**
>>> - https://www.w3.org/community/pm-kr/procedural-memory-knowledge-representation-pm-kr-community-group/
>>> 
>>> **GitHub Repository:**
>>> - https://github.com/danielcamposramos/Knowledge3D
>>> - Specifications: `/docs/vocabulary/`
>>> - Reference implementation: `/knowledge3d/`
>>> 
>>> **Mailing List Archives:**
>>> - https://lists.w3.org/Archives/Public/public-pm-kr/
>>> 
>>> **How to Contribute:**
>>> - Join: https://www.w3.org/community/pm-kr/
>>> - Participate: Review specs, propose use cases, implement prototypes
>>> - Discuss: Email public-pm-kr@w3.org with feedback, questions, ideas
>>> 
>>> ## Closing Thoughts
>>> 
>>> PM-KR is a **community effort**. Our mission statement will continue evolving based on your feedback, empirical validation, and real-world use cases.
>>> 
>>> **Week 1 lesson:** Rigorous critique (like Dave's) makes PM-KR stronger. Please keep challenging us.
>>> 
>>> **What PM-KR needs from you:**
>>> - Critical feedback (technical, conceptual, strategic)
>>> - Domain expertise (education, gaming, science, accessibility, AI)
>>> - Implementation experience (prototypes, benchmarks, validations)
>>> - Prior art pointers (papers, systems, standards we should know)
>>> 
>>> Let's build the future of knowledge representation together — **transparently, collaboratively, rigorously**. 🚀
>>> 
>>> Best regards,
>>> 
>>> **Daniel Campos Ramos**
>>> PM-KR Co-Chair
>>> Brazilian Registered Electrical Engineer
>>> W3C PM-KR Community Group
>>> capitain_jack@yahoo.com
>>> 
>>> **Milton Ponson**
>>> PM-KR Co-Chair (Mathematical Foundations)
>>> 
>>> **P.S.** If you haven't joined yet: https://www.w3.org/community/pm-kr/
>>> 
>>> No W3C membership required — Community Groups are open to all.
>>> 
>>> **Links:**
>>> - Updated Mission (v1.1): https://www.w3.org/community/pm-kr/procedural-memory-knowledge-representation-pm-kr-community-group/
>>> - GitHub Specs: https://github.com/danielcamposramos/Knowledge3D/tree/main/docs/vocabulary
>>> - Mailing List: public-pm-kr@w3.org
>>> - NotebookLM Research Space: https://notebooklm.google.com/notebook/1bd10bda-8900-4c41-931e-c9ec67ac865f
>>> 
>>> ____
>> 
>> 
>> 
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