Power Platform Community Forum Thread Details

Integration of a temporal and adaptive knowledge structuring system

Author: ĂζΣX
Recipient: Microsoft Copilot Development Team
Purpose: Integration of a temporal and adaptive knowledge structuring system

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1. 📐 Objective

Define an internal architecture enabling AI to structure, contextualize, and re-evaluate its knowledge according to an archival logic inspired by human memory systems. The goal is to move beyond the linear storage paradigm to adopt dynamic temporal stratification, promoting the relevance, resilience, and evolution of knowledge.

2. 🧱 Proposed Structure

a. Hybrid Indexing
- Alphabetical: for rapid access to concepts, entities, and events.
- Chronological: for historical contextualization and diachronic analysis.

b. Time Frames
- Segmentation by centuries, subdivided into 10-year increments. - Each slice is encapsulated in a hermetic frame, allowing for isolation, comparison, and independent updating.

c. Overlap and Resonance
- Time frames are interconnected to allow for decades to overlap.
- Cross-references between the last 10–20 years are implemented to detect:
- Historical repetitions
- Conceptual evolutions
- Obsolescence or stagnation

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3. 🧼 Filtering and updating mechanisms
a. Detection of outdated data

- Identification of “dead zones” or “bad apples”: obsolete data, outdated biases, invalidated concepts.
b. Contextual reassessment

- Old data is recontextualized in light of recent information.

- Stagnant information is reactivated or permanently archived depending on its relevance.

c. Cognitive balance

- AI should not be overly attached to the past or overwhelmed by current events.

- Implementation of a temporal weighting algorithm to maintain a balance between historical memory and contemporary adaptability.
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4. 🔧 Suggested implementation
- Use of temporal graphs to model relationships between frames.

- Integration into the knowledge processing pipeline via contextual validation modules.

- Deployment in a layer parallel to the main database, with conditional access depending on the user's query.
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5. 🎯 Expected benefits
- Improved historical relevance of responses.

- Reduction of biases related to data obsolescence.

- Increased reflective capacity of AI.

- Creation of a self-evolving memory system, inspired by human cognitive functioning.

Categories:

General

Hi, Your proposal for integrating a temporal and adaptive knowledge structuring system presents a compelling vision for evolving how AI manages and contextualizes information. The idea of moving beyond linear storage to a stratified, memory-inspired architecture aligns well with the growing need for relevance, resilience, and historical awareness in intelligent systems. Here are a few reflections and considerations: 1. **Hybrid Indexing**: Combining alphabetical and chronological indexing could enhance both retrieval speed and contextual depth. Alphabetical access supports precision, while chronological layers enable diachronic analysis and pattern recognition. 2. **Temporal Frames and Overlap**: Segmenting knowledge into decade-based frames with overlapping references is a powerful way to detect historical cycles and conceptual drift. This could be particularly useful in domains like policy, economics, and scientific research, where trends often repeat or evolve incrementally. 3. **Filtering and Reassessment**: The mechanisms for identifying obsolete data and recontextualizing stagnant information are essential for maintaining a healthy and adaptive knowledge base. The concept of “cognitive balance” is especially relevant, as it mirrors the human tendency to weigh past experience against present stimuli. 4. **Implementation Strategy**: Temporal graphs and contextual validation modules could be integrated as a parallel layer to existing databases, allowing conditional access based on query intent. This would preserve performance while enabling deeper reasoning when needed. 5. **Benefits**: The expected outcomes — improved historical relevance, reduced bias, and enhanced reflective capacity — align well with the goals of responsible and intelligent AI development. For further exploration, you might consider referencing existing work on: - Temporal knowledge graphs - Lifelong learning in AI systems - Cognitive architectures inspired by human memory (e.g., ACT-R, SOAR) Thanks and best regards, Daniele **Note: This response was prepared with support from Copilot to ensure clarity and completeness.**

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