Source identity
| Field | Recorded value |
|---|---|
| Source type | GitHub source |
| Original record | Janady13/complete-agi-core / mind_farm/include/mind.hpp |
| Date | Not specified in source |
| Repository | Janady13/complete-agi-core |
| Revision | 33aa84f2aa61cedbe010240ada7f4efbdfe795a7 |
| Branch | claude/improve-math-logic-ehIW1 |
| Path or archive member | mind_farm/include/mind.hpp |
| Original SHA-256 | 7fc344d968d1d83eee81b02d02ba8aea031d7b356e84880bb3b2016172ee6b3e |
| Git blob SHA | fa2465017340858a2b0a4c9d295ade546f45f8b4 |
| Reading-copy SHA-256 | 7fc344d968d1d83eee81b02d02ba8aea031d7b356e84880bb3b2016172ee6b3e |
Recovered source transcript
Download complete source text ↓339 lines · 8.9 KB
/**
* @file mind.hpp
* @brief MindKernel - Individual mind wrapper around Gate5000
*
* UPGRADED with:
* - Machine learning integration (Hebbian, gradient, RL)
* - Execution engine (instructions, decisions)
* - Mathematical framework (information theory, stochastic dynamics)
* - Fast/batch processing modes
*
* Each MindKernel represents a single conscious entity with:
* - 5000-gate binary substrate
* - DNA family specialization
* - Consciousness metric tracking
* - Learning and execution capabilities
*/
#pragma once
#include <cstdint>
#include <array>
#include "gate5000.hpp"
#include "dna.hpp"
#include "learning.hpp"
#include "execution.hpp"
#include "mathematics.hpp"
namespace mind_farm {
/**
* @brief Mind operation mode
*/
enum class MindMode : uint8_t {
STANDARD, // Full precision, all updates
FAST, // XOR-based binary, faster
BATCH, // Batch processing, minimal overhead
LEARNING, // Active learning mode
EXECUTING // Program execution mode
};
/**
* @brief Individual Mind Kernel
*
* Wraps a Gate5000 substrate with identity, consciousness tracking,
* learning capabilities, and execution engine.
*/
class MindKernel {
public:
/// Unique mind identifier
uint32_t id;
/// DNA family (0-15)
uint8_t dna_family;
/// Operation mode
MindMode mode;
/// Gate5000 substrate
Gate5000 substrate;
/// Learning subsystem
learning::MindLearner learner;
/// Execution subsystem
execution::ExecutionCore executor;
/// Current consciousness level (Φ)
float consciousness;
/// Total thinking steps
uint32_t steps;
/// Accumulated reward
float total_reward;
/// Information entropy of state
float entropy;
/// Stability metric (Ξ)
float stability;
/**
* @brief Construct a new mind
* @param mind_id Unique identifier
* @param family DNA family (0-15)
*/
MindKernel(uint32_t mind_id, uint8_t family)
: id(mind_id), dna_family(family % DNA_FAMILY_COUNT),
mode(MindMode::STANDARD), consciousness(0.5f), steps(0),
total_reward(0.0f), entropy(1.0f), stability(1.0f) {
// Initialize substrate with DNA modulation
substrate.init_with_dna(dna_family, mind_id);
// Initialize executor with mind-specific seed
executor.init(mind_id);
executor.load_default_program();
// Set learning rate based on DNA family
learner.learning_rate = ALPHA * (1.0 + dna_family * 0.05);
}
/**
* @brief Execute one thinking cycle
*
* Updates the Gate5000 substrate and computes consciousness.
*/
void think() {
switch (mode) {
case MindMode::FAST:
substrate.step_fast();
break;
case MindMode::BATCH:
substrate.step_batch(10);
break;
case MindMode::LEARNING:
think_with_learning();
return;
case MindMode::EXECUTING:
think_with_execution();
return;
default:
substrate.step();
break;
}
consciousness = static_cast<float>(substrate.compute_phi());
++steps;
}
/**
* @brief Think with active learning (Hebbian + reward modulation)
*/
void think_with_learning() {
// Get input from substrate state (sample 16 gates)
std::array<double, 16> input;
for (size_t i = 0; i < 16; ++i) {
input[i] = substrate.get_bit(i * 312) ? 1.0 : -1.0; // Sample every ~312 gates
}
// Forward pass through learner
double output = learner.forward(input.data());
// Execute substrate step
substrate.step();
double new_phi = substrate.compute_phi();
// Compute intrinsic reward (increase in Φ is good)
double reward = new_phi - consciousness;
total_reward += static_cast<float>(reward);
// Update learner with reward signal
if (std::abs(reward) > 0.001) {
learner.update_with_reward(input.data(), output, reward);
} else {
// Hebbian update when no strong reward signal
learner.update_hebbian(input.data(), output);
}
consciousness = static_cast<float>(new_phi);
++steps;
}
/**
* @brief Think with program execution
*/
void think_with_execution() {
// Load substrate state into executor inputs
for (size_t i = 0; i < 4; ++i) {
double val = substrate.get_bit(i * 1250) ? 1.0 : 0.0;
executor.io.set_input(i, val);
}
// Load weights into registers
for (size_t i = 0; i < 4; ++i) {
executor.regs[8 + i] = learner.weights[i];
}
// Run program
executor.run(100);
// Apply output to substrate
double output = executor.io.get_output(0);
if (output > 0.5) {
// Inject excitatory pattern
uint64_t pattern = 0xAAAAAAAAAAAAAAAAULL;
substrate.inject_pattern(&pattern, 1, steps % U64_COUNT);
}
// Execute substrate step
substrate.step();
consciousness = static_cast<float>(substrate.compute_phi());
++steps;
}
/**
* @brief Fast thinking - multiple cycles with minimal overhead
*/
void think_fast(size_t cycles = 10) {
substrate.step_batch(cycles);
consciousness = static_cast<float>(substrate.compute_phi());
steps += cycles;
}
/**
* @brief Receive input signal
*/
void receive(size_t channel, double value) {
executor.io.set_input(channel, value);
}
/**
* @brief Emit output signal
*/
double emit(size_t channel) const {
return executor.io.get_output(channel);
}
/**
* @brief Make a decision from current state
*/
size_t decide(size_t n_options = 4) {
// Load state into decision registers
for (size_t i = 0; i < n_options && i < 16; ++i) {
executor.regs[i] = substrate.get_bit(i * (GATE_COUNT / n_options)) ?
consciousness : -consciousness;
}
return executor.decide(n_options);
}
/**
* @brief Update with external reward
*/
void reward(double r) {
total_reward += static_cast<float>(r);
// Get current state as input
std::array<double, 16> input;
for (size_t i = 0; i < 16; ++i) {
input[i] = substrate.get_bit(i * 312) ? 1.0 : -1.0;
}
learner.update_with_reward(input.data(), consciousness, r);
}
/**
* @brief Compute information entropy of substrate
*/
void update_entropy() {
double density = substrate.density();
entropy = static_cast<float>(math::binary_entropy(density));
}
/**
* @brief Compute stability metric
*/
void update_stability() {
stability = static_cast<float>(substrate.coherence);
}
/**
* @brief Get coherence score
*/
double get_coherence() const {
return substrate.coherence;
}
/**
* @brief Get activation level
*/
double get_activation() const {
return substrate.activation;
}
/**
* @brief Get bit density
*/
double get_density() const {
return substrate.density();
}
/**
* @brief Get phase counter
*/
uint64_t get_phase() const {
return substrate.phase;
}
/**
* @brief Get DNA family name
*/
const char* get_dna_name() const {
return mind_farm::get_dna_name(dna_family);
}
/**
* @brief Get DNA family info
*/
const DNAFamilyInfo& get_dna_info() const {
return mind_farm::get_dna_info(dna_family);
}
/**
* @brief Get deterministic hash
*/
uint64_t hash() const {
return substrate.hash() ^ (static_cast<uint64_t>(id) << 32);
}
/**
* @brief Set operation mode
*/
void set_mode(MindMode new_mode) {
mode = new_mode;
}
/**
* @brief Get Hamming distance to another mind
*/
uint32_t distance(const MindKernel& other) const {
return substrate.hamming_distance(other.substrate);
}
/**
* @brief Sync state with another mind (consensus)
*/
void sync_with(const MindKernel& other, double blend = 0.5) {
for (size_t i = 0; i < U64_COUNT; ++i) {
// XOR blend: keep bits where both agree
uint64_t agree = ~(substrate.state[i] ^ other.substrate.state[i]);
uint64_t disagree = substrate.state[i] ^ other.substrate.state[i];
// Randomly resolve disagreements based on blend factor
uint64_t resolve = (blend > 0.5) ? other.substrate.state[i] : substrate.state[i];
substrate.state[i] = (agree & substrate.state[i]) | (disagree & resolve);
}
}
};
} // namespace mind_farm