This guide provides practical implementation details for creating a distributed network of cognitive agents using the existing ggml infrastructure.
For architectural overview, see the Distributed Cognitive Architecture. For grammar usage examples, see the Cognitive Grammar Examples.
#include "ggml.h"
#include "ggml-rpc.h"
#include "cognitive-agent.h"
// Cognitive agent state structure
struct cognitive_agent {
ggml_context* ctx;
ggml_backend_t backend;
// Cognitive components
struct hypergraph_memory* memory;
struct task_orchestrator* tasks;
struct reasoning_engine* reasoning;
struct attention_economy* attention;
// Network identity
uint64_t agent_id;
char endpoint[256];
float attention_weight;
};
// Initialize a cognitive agent
cognitive_agent* create_cognitive_agent(const char* endpoint) {
cognitive_agent* agent = malloc(sizeof(cognitive_agent));
// Initialize ggml context
agent->ctx = ggml_init({.mem_size = 128*1024*1024});
// Setup RPC backend for distributed operation
agent->backend = ggml_backend_rpc_init(endpoint);
// Initialize cognitive subsystems
agent->memory = init_hypergraph_memory(agent->ctx);
agent->tasks = init_task_orchestrator();
agent->reasoning = init_reasoning_engine(agent->ctx);
agent->attention = init_attention_economy();
agent->agent_id = generate_agent_id();
strcpy(agent->endpoint, endpoint);
agent->attention_weight = 1.0f;
return agent;
}// Hypergraph node encoded as tensor
struct hypergraph_node {
ggml_tensor* data; // Node content as tensor
ggml_tensor* embedding; // Semantic embedding
uint32_t node_type; // Concept/Link/etc
float truth_value; // PLN truth value
float confidence; // PLN confidence
uint64_t creation_time; // Timestamp
};
// Hypergraph memory system
struct hypergraph_memory {
ggml_context* ctx;
// Storage
hypergraph_node** nodes;
size_t node_count;
size_t capacity;
// Indexing for efficient retrieval
struct embedding_index* semantic_index;
struct temporal_index* time_index;
};
// Add knowledge to hypergraph
void add_knowledge(hypergraph_memory* mem, const char* concept,
float* embedding, size_t emb_size) {
// Create tensor for concept
ggml_tensor* concept_tensor = ggml_new_tensor_1d(mem->ctx, GGML_TYPE_F32,
strlen(concept));
memcpy(concept_tensor->data, concept, strlen(concept));
// Create embedding tensor
ggml_tensor* emb_tensor = ggml_new_tensor_1d(mem->ctx, GGML_TYPE_F32,
emb_size);
memcpy(emb_tensor->data, embedding, emb_size * sizeof(float));
// Create hypergraph node
hypergraph_node* node = malloc(sizeof(hypergraph_node));
node->data = concept_tensor;
node->embedding = emb_tensor;
node->node_type = NODE_TYPE_CONCEPT;
node->truth_value = 0.8f; // Initial belief
node->confidence = 0.9f; // High confidence
node->creation_time = get_timestamp();
// Add to memory
if (mem->node_count >= mem->capacity) {
expand_memory(mem);
}
mem->nodes[mem->node_count++] = node;
// Update indices
update_semantic_index(mem->semantic_index, node);
update_temporal_index(mem->time_index, node);
}// Cognitive grammar rule
struct cognitive_rule {
char name[64];
char gbnf_pattern[512]; // GBNF grammar pattern
float activation_threshold; // When to trigger
ggml_tensor* preconditions; // Required context
ggml_tensor* postconditions; // Expected results
};
// Grammar-based task decomposition
struct task_orchestrator {
cognitive_rule* rules;
size_t rule_count;
// Current task state
ggml_tensor* current_goal;
ggml_tensor* context_state;
// Grammar parser integration
struct llama_grammar* grammar_parser;
};
// Decompose complex task using cognitive grammar
task_plan* decompose_task(task_orchestrator* orch, ggml_tensor* goal) {
task_plan* plan = create_task_plan();
// Find applicable rules
for (size_t i = 0; i < orch->rule_count; i++) {
cognitive_rule* rule = &orch->rules[i];
// Check if rule preconditions match current context
float match_score = compute_tensor_similarity(
rule->preconditions, orch->context_state);
if (match_score > rule->activation_threshold) {
// Apply rule to decompose goal
subtask_list* subtasks = apply_cognitive_rule(rule, goal);
add_subtasks_to_plan(plan, subtasks);
}
}
return plan;
}// Enhanced RPC message with cognitive metadata
struct cognitive_rpc_message {
rpc_tensor base_tensor;
// Cognitive metadata
float attention_weight;
float salience_score;
uint32_t cognitive_type;
uint64_t source_agent;
uint64_t target_agent;
char context[256];
uint32_t recursion_depth;
uint64_t timestamp;
};
// Send cognitive tensor to another agent
void send_cognitive_tensor(cognitive_agent* sender, uint64_t target_agent_id,
ggml_tensor* tensor, float attention_weight) {
cognitive_rpc_message msg = {0};
// Serialize base tensor
msg.base_tensor = serialize_tensor(tensor);
// Add cognitive metadata
msg.attention_weight = attention_weight;
msg.salience_score = compute_salience(tensor, sender->attention);
msg.cognitive_type = infer_cognitive_type(tensor);
msg.source_agent = sender->agent_id;
msg.target_agent = target_agent_id;
msg.recursion_depth = get_current_recursion_depth();
msg.timestamp = get_timestamp();
// Route based on attention economy
if (should_send_message(sender->attention, &msg)) {
transmit_cognitive_message(&msg);
// Update attention allocation
update_attention_weights(sender->attention, &msg);
}
}
// Receive and process cognitive tensor
void process_incoming_tensor(cognitive_agent* receiver,
cognitive_rpc_message* msg) {
// Deserialize tensor
ggml_tensor* tensor = deserialize_tensor(receiver->ctx, &msg->base_tensor);
// Check attention allocation
if (msg->attention_weight < receiver->attention->min_threshold) {
// Insufficient attention, defer processing
defer_message(receiver->attention, msg);
return;
}
// Process based on cognitive type
switch (msg->cognitive_type) {
case COGNITIVE_TYPE_MEMORY:
integrate_memory(receiver->memory, tensor, msg);
break;
case COGNITIVE_TYPE_TASK:
process_task_request(receiver->tasks, tensor, msg);
break;
case COGNITIVE_TYPE_REASONING:
apply_reasoning(receiver->reasoning, tensor, msg);
break;
case COGNITIVE_TYPE_ATTENTION:
update_attention_economy(receiver->attention, tensor, msg);
break;
}
// Generate response if needed
if (requires_response(msg)) {
generate_cognitive_response(receiver, msg);
}
}// Attention economy state
struct attention_economy {
float total_attention; // Total available attention
float allocated_attention; // Currently allocated
// Attention allocation per cognitive function
float memory_allocation;
float reasoning_allocation;
float communication_allocation;
float self_modification_allocation;
// Economic parameters
float min_threshold; // Minimum attention to process
float decay_rate; // Attention decay over time
float novelty_bonus; // Bonus for novel information
// Performance tracking
float* performance_history;
size_t history_size;
};
// Compute salience score for tensor
float compute_salience(ggml_tensor* tensor, attention_economy* attention) {
float salience = 0.0f;
// Novelty component
float novelty = compute_novelty(tensor, attention);
salience += novelty * attention->novelty_bonus;
// Relevance to current goals
float relevance = compute_goal_relevance(tensor);
salience += relevance * 0.5f;
// Utility for decision making
float utility = estimate_utility(tensor);
salience += utility * 0.3f;
return salience;
}
// Economic attention allocation
void allocate_attention(attention_economy* attention,
cognitive_rpc_message* msg) {
float required_attention = msg->attention_weight;
// Check if sufficient attention available
float available = attention->total_attention - attention->allocated_attention;
if (available < required_attention) {
// Need to reallocate attention
reallocate_attention(attention, required_attention);
}
// Allocate attention to appropriate cognitive function
switch (msg->cognitive_type) {
case COGNITIVE_TYPE_MEMORY:
attention->memory_allocation += required_attention;
break;
case COGNITIVE_TYPE_REASONING:
attention->reasoning_allocation += required_attention;
break;
// ... other types
}
attention->allocated_attention += required_attention;
}// Self-modification rules
struct modification_rule {
char condition[256]; // When to apply modification
char action[256]; // What modification to make
float success_threshold; // Performance threshold
uint32_t application_count; // How many times applied
};
// Self-modification engine
struct self_modification_engine {
modification_rule* rules;
size_t rule_count;
// Performance monitoring
float current_performance;
float target_performance;
// Modification history
struct modification_log* history;
};
// Monitor performance and trigger self-modification
void monitor_and_adapt(cognitive_agent* agent) {
float current_perf = measure_performance(agent);
if (current_perf < agent->attention->performance_history[0] * 0.9f) {
// Performance degradation detected
trigger_self_modification(agent);
}
// Update performance history
update_performance_history(agent->attention, current_perf);
}
// Apply self-modification
void trigger_self_modification(cognitive_agent* agent) {
// Analyze current cognitive state
cognitive_state_analysis analysis = analyze_cognitive_state(agent);
// Find applicable modification rules
for (size_t i = 0; i < agent->reasoning->mod_engine->rule_count; i++) {
modification_rule* rule = &agent->reasoning->mod_engine->rules[i];
if (should_apply_modification(rule, &analysis)) {
apply_modification(agent, rule);
// Monitor results
schedule_performance_evaluation(agent, rule);
}
}
}int main() {
// Create two cognitive agents
cognitive_agent* agent1 = create_cognitive_agent("localhost:8001");
cognitive_agent* agent2 = create_cognitive_agent("localhost:8002");
// Add some knowledge to agent1
float concept_embedding[128] = {/* embedding values */};
add_knowledge(agent1->memory, "artificial_intelligence",
concept_embedding, 128);
// Create a reasoning task
ggml_tensor* task = create_reasoning_task(agent1->ctx,
"What is consciousness?");
// Agent1 processes task and may need to consult agent2
if (requires_collaboration(task)) {
// Send cognitive state to agent2
ggml_tensor* cognitive_state = extract_relevant_state(agent1, task);
send_cognitive_tensor(agent1, agent2->agent_id,
cognitive_state, 0.8f);
// Wait for response and integrate
cognitive_rpc_message* response = wait_for_response(agent1, 5000);
if (response) {
integrate_response(agent1, response);
}
}
// Generate final answer
ggml_tensor* answer = generate_answer(agent1, task);
// Cleanup
cleanup_cognitive_agent(agent1);
cleanup_cognitive_agent(agent2);
return 0;
}Add to your CMakeLists.txt:
# Cognitive agent system
add_library(cognitive-agents STATIC
src/cognitive-agent.c
src/hypergraph-memory.c
src/task-orchestrator.c
src/reasoning-engine.c
src/attention-economy.c
src/self-modification.c
)
target_link_libraries(cognitive-agents
ggml
ggml-cpu
${CMAKE_THREAD_LIBS_INIT}
)
# Example cognitive network
add_executable(cognitive-network examples/cognitive-network.c)
target_link_libraries(cognitive-network cognitive-agents)mkdir build && cd build
cmake .. -DGGML_RPC=ON
make -j$(nproc)
# Run example
./bin/cognitive-network// Test hypergraph memory
void test_hypergraph_memory() {
ggml_context* ctx = ggml_init({.mem_size = 1024*1024});
hypergraph_memory* mem = init_hypergraph_memory(ctx);
// Add test knowledge
float embedding[64];
for (int i = 0; i < 64; i++) embedding[i] = i / 64.0f;
add_knowledge(mem, "test_concept", embedding, 64);
// Verify retrieval
hypergraph_node* retrieved = find_concept(mem, "test_concept");
assert(retrieved != NULL);
assert(retrieved->truth_value > 0.0f);
cleanup_hypergraph_memory(mem);
ggml_free(ctx);
}
// Test attention economy
void test_attention_economy() {
attention_economy* attention = init_attention_economy();
attention->total_attention = 1.0f;
// Create test message
cognitive_rpc_message msg = {0};
msg.attention_weight = 0.3f;
msg.salience_score = 0.8f;
// Test allocation
allocate_attention(attention, &msg);
assert(attention->allocated_attention == 0.3f);
cleanup_attention_economy(attention);
}void test_agent_communication() {
// Create test agents
cognitive_agent* sender = create_cognitive_agent("localhost:9001");
cognitive_agent* receiver = create_cognitive_agent("localhost:9002");
// Test communication
ggml_tensor* test_tensor = create_test_tensor(sender->ctx);
send_cognitive_tensor(sender, receiver->agent_id, test_tensor, 0.5f);
// Verify reception
sleep(1); // Allow processing time
assert(receiver->memory->node_count > 0);
cleanup_cognitive_agent(sender);
cleanup_cognitive_agent(receiver);
}This implementation guide provides a practical foundation for building the distributed cognitive architecture using existing ggml infrastructure. The system can be incrementally developed and tested, starting with basic agent communication and gradually adding more sophisticated cognitive capabilities.