A.I. programming in Prolog and Assembler

I asked DeepSeek: “Is there a way to make logical inferences using bit-encoded data?“

DeepSeek answered:

Yes, it is possible to make logical inferences using bit-encoded data. Bit-encoded data represents information in binary form (0s and 1s), and logical operations can be performed on this data to derive new information or make inferences. Here’s how you can approach it:

1. Logical Operations on Bits

• AND (&): Returns 1 if both bits are 1, otherwise 0.
• OR (|): Returns 1 if at least one bit is 1, otherwise 0.
• XOR (^): Returns 1 if the bits are different, otherwise 0.
• NOT (~): Flips the bit (1 becomes 0, and 0 becomes 1).
• Shift Operations (<<, >>): Shift bits left or right.

These operations can be used to manipulate and analyze bit-encoded data.

2. Bitwise Logical Inference

• If you encode logical propositions as bits (e.g., A = 1, B = 0), you can use bitwise operations to perform logical inference.
• For example:
  • A AND B can be computed as A & B.
  • A OR B can be computed as A | B.
  • NOT A can be computed as ~A (though be cautious with the bit width).

3. Encoding Data for Logical Inference

• You can encode multiple logical variables into a single integer by assigning each variable to a specific bit position.
• For example:
  • Let A be the 0th bit, B be the 1st bit, and C be the 2nd bit.
  • The integer 5 (binary 101) would represent A = 1, B = 0, C = 1.

4. Example: Logical Inference

Suppose you have the following rules:

• If A is true and B is false, then C is true.
• Encode A, B, and C as bits in an integer.

You can implement this inference using bitwise operations:

In Python:

data = 0b101 # A=1, B=0, C=1
A = (data >> 0) & 1 # Extract bit 0 (A)
B = (data >> 1) & 1 # Extract bit 1 (B)
C = (data >> 2) & 1 # Extract bit 2 (C)

if A == 1 and B == 0:
C = 1
data = (data & ~0b100) | (C << 2) # Update C in the data

print(bin(data)) # Output: 0b101 (C remains 1)

5. Applications

• Boolean Algebra: Solve logical expressions using bitwise operations.
• Rule-Based Systems: Encode rules and perform inference on bit-encoded data.
• Compression: Represent multiple boolean values compactly using bits.
• Hardware-Level Logic: Simulate digital circuits using bitwise operations.

6. Limitations

• Bitwise operations are limited to the number of bits in the data type (e.g., 32 or 64 bits).
• Complex logical inferences may require multiple steps or additional encoding schemes.

By leveraging bitwise operations, you can efficiently perform logical inferences on bit-encoded data, making it a powerful tool for low-level programming and optimization.

——————— (END of DeepSeek’s Answer) ———————

My comment:
Since a long time ago, I’ve been using bit-encoding for a wide variety of tasks, implemented in Pure Assembly Language, callable as predicates for Prolog or as functions for ‘C’ / C++, including code for logical inferences, but I never implemented Prolog Unification using bit-encoding, till now. Well, amazingly enough, DeepSeek also explained how to implement classic Prolog Unification (of variables) encoded in bits

See also:

• DeepSeek on implementing classic Prolog Unification (of variables) encoded in bits
• Prolog vs LLMs: A Complementary Approach to AI (according to DeepSeek)