Introducing Constructivist AI: A New Approach to AI

Project: Constructivist AI
Version: v0.1.0-alpha
Author: DanexCodr
License: MIT

What is Constructivist AI?

Constructivist AI is a research implementation of a cognitive architecture that learns structured patterns from sequential data. Unlike statistical machine learning approaches that operate as black boxes, this system builds explicit, interpretable representations of patterns and their structural properties.

The Core Innovation: Self-Accelerating Learning

The most interesting aspect of this architecture is its ability to improve its own learning efficiency over time. Here's how it works:

1. Learns patterns from minimal examples (2-3 sequences)
2. Discovers pattern properties like commutativity and optionality
3. Uses those properties to learn new patterns more efficiently
4. Achieves one-shot learning in appropriate contexts

Real Example from the System:

After learning: "cat and dog" and "dog and cat"
→ Discovers: "and" is commutative (creates [C] and [C] pattern)

Later receives: "cat and dog are mammals" (only one example!)
→ Recognizes: "cat and dog" matches commutative pattern PF2
→ Infers: "dog and cat are mammals" must also be valid
→ Learns: Composite pattern [PF2] are [X] from single example

This demonstrates property-based learning acceleration - the system uses discovered structural properties to learn faster.

Technical Distinctions

Why this isn't just another pattern miner:

1. Explicit Property Representation: Commutativity, optionality, etc. are explicitly represented, not just statistically inferred
2. Hierarchical Composition: Patterns can nest (PF2 inside PF3) to form complex structures
3. Transparent Reasoning: All learned patterns are examinable and explainable
4. Cognitive Architecture: Inspired by constructivist learning theory from psychology

Research Context

This project explores an alternative path between:

· Classical Symbolic AI (hand-coded rules, no learning)
· Statistical ML/LLMs (black-box learning, massive data requirements)

It demonstrates how a system can discover its own structural operations from data rather than having them pre-programmed.

Current Status

v0.1.0-alpha implements:

· Basic pattern learning from sequences
· Commutativity detection
· Optionality detection
· Pattern family formation
· One-shot learning via property reuse
· Transparent pattern examination

Limitations (acknowledged):

· Early research prototype
· Exact token matching required
· Sequential, symbolic input only

Invitation to Explore

This is fundamentally a research exploration into cognitive architectures and learning systems. I'm sharing it early to:

1. Get feedback on the architectural approach
2. Find collaborators interested in alternative AI paradigms
3. Start discussions about transparent, interpretable learning systems

Questions for the community:

· What domains might benefit from this transparent, compositional approach?
· How could this architecture complement existing ML approaches?
· What cognitive science principles should inform such systems?

Getting Started

git clone https://github.com/DanexCodr/constructivist-ai.git
cd constructivist-ai
javac -d bin src/danexcodr/ai/**/*.java src/danexcodr/ai/core/*.java src/danexcodr/ai/pattern/*.java
java -cp bin danexcodr.ai.Main

Join the Discussion

I'm particularly interested in discussions around:

· Cognitive architectures and learning theory
· Transparent AI and explainable systems
· Alternative paradigms to current statistical approaches
· Applications in education, linguistics, or scientific discovery

This isn't positioned as a replacement for current ML - rather, it's an exploration of a different kind of intelligence: one that builds explicit structures and learns how to learn better over time.

Check out the repository and let me know what you think!