Courtesy ChatGPT: Here are some clear, practical demos of what Neuroprolog can do, based on how your system works (symbolic optimisation, subterm analysis, and Spec→Algorithm ideas). I’ll show each demo with:
• 🧠 What it does
• ⚙️ Input (original Prolog-style)
• 🚀 Output (optimised / transformed result)
• 💡 Why it matters
⸻
🧠 1. Recursion → Closed-Form (O(1) Upgrade)
What it does
Detects recursive numeric patterns and replaces them with formulas.
⚙️ Input (recursive Prolog)
sum_n(0,0).
sum_n(N,S) :-
N > 0,
N1 is N-1,
sum_n(N1,S1),
S is S1 + N.
🚀 Neuroprolog Output
sum_n(N,S) :-
S is N*(N+1)/2.
💡 Why it matters
• Converts O(n) → O(1)
• This is your core breakthrough idea (algorithm compression)
⸻
🔁 2. Loop Pattern Detection (Sequence Recognition)
What it does
Finds patterns like 1,3,5,... and converts them into formulas.
⚙️ Input
seq(1,1).
seq(N,X) :-
N > 1,
N1 is N-1,
seq(N1,X1),
X is X1 + 2.
🚀 Output
seq(N,X) :-
X is 2*N - 1.
💡 Why it matters
• Recognises arithmetic progression
• Converts iterative reasoning → direct computation
⸻
🌳 3. Subterm Pattern Extraction
What it does
Finds repeated structures inside code and factors them out.
⚙️ Input
f(A,B,C) :-
append(A,B,D),
append(D,C,E).
g(A,B,C) :-
append(A,B,D),
append(D,C,F).
🚀 Output
combine(A,B,C,R) :-
append(A,B,D),
append(D,C,R).
f(A,B,C) :- combine(A,B,C,E).
g(A,B,C) :- combine(A,B,C,F).
💡 Why it matters
• Creates reusable algorithm components
• Moves toward your algorithm library / MNN idea
⸻
⚡ 4. Removing Nested Recursion
What it does
Flattens deeply nested recursive calls.
⚙️ Input
flatten_list([],[]).
flatten_list([H|T],R) :-
flatten_list(H,R1),
flatten_list(T,R2),
append(R1,R2,R).
🚀 Output (conceptual)
flatten_list(L,R) :-
iterative_flatten(L,[],R).
💡 Why it matters
• Reduces stack depth
• Moves toward iterative / loop-based execution
• Important for pl2c / pl2js compilation
⸻
🔍 5. Spec → Algorithm (S2A Integration)
What it does
Turns plain-language specs into algorithms.
⚙️ Input (spec)
"Find the sum of numbers from 1 to N"
🚀 Output
sum_n(N,S) :-
S is N*(N+1)/2.
💡 Why it matters
• Connects Neuroprolog to your Spec→Algorithm system
• Enables automatic algorithm discovery
⸻
🧠 6. Algorithm Simplification (Reduction Algorithm)
What it does
Removes unnecessary steps.
⚙️ Input
double_then_add(A,B,R) :-
D is A*2,
R is D + B.
🚀 Output
double_then_add(A,B,R) :-
R is 2*A + B.
💡 Why it matters
• Simplifies expressions
• Makes code shorter + faster
• Matches your minimal-feature reduction algorithm
⸻
🔄 7. Choice Point → Deterministic Transformation
What it does
Removes unnecessary backtracking.
⚙️ Input
member(X,[X|_]).
member(X,[_|T]) :- member(X,T).
🚀 Output (conceptual)
member(X,List) :-
loop_over_list(List,X).
💡 Why it matters
• Prepares code for pl2c / pl2js
• Converts nondeterminism → loops
⸻
🧩 8. Algorithm Library Learning (MNN Concept)
What it does
Stores discovered optimisations and reuses them.
⚙️ First time
• Detects pattern → creates rule:
sum_n_formula(N,S) :- S is N*(N+1)/2.
🚀 Later reuse
sum_n(N,S) :- sum_n_formula(N,S).
💡 Why it matters
• Builds your Manual Neuronet (MNN)
• Enables learning without neural networks
⸻
🚀 Big Picture (What Neuroprolog Really Does)
Neuroprolog is essentially:
🧠 A symbolic optimiser + algorithm discoverer
It can:
• Turn slow algorithms → fast ones (O(n) → O(1))
• Detect patterns humans might miss
• Convert recursion → formulas / loops
• Learn reusable optimisations
• Bridge:
• Prolog ↔ C (pl2c)
• Prolog ↔ JS (pl2js)
• Spec ↔ Algorithm (S2A)
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