This document provides practical examples of cognitive grammars that can be used with the distributed agent system, building on the existing GBNF grammar system in llama.cpp.
# Cognitive task decomposition grammar
root ::= cognitive-task
cognitive-task ::=
"task(" task-name ")" ws
"preconditions(" precondition-list ")" ws
"decomposition(" subtask-list ")" ws
"postconditions(" postcondition-list ")"
task-name ::= [a-zA-Z_][a-zA-Z0-9_]*
precondition-list ::= precondition (ws "," ws precondition)*
precondition ::= logical-statement | tensor-condition
subtask-list ::= subtask (ws "," ws subtask)*
subtask ::= cognitive-task | primitive-action
postcondition-list ::= postcondition (ws "," ws postcondition)*
postcondition ::= logical-statement | tensor-condition
logical-statement ::=
"belief(" concept "," truth-value "," confidence-value ")" |
"knowledge(" concept "," embedding-ref ")" |
"goal(" objective "," priority-value ")"
tensor-condition ::=
"tensor_similarity(" tensor-ref "," tensor-ref "," threshold ")" |
"tensor_norm(" tensor-ref "," norm-type "," threshold ")"
primitive-action ::=
"send_message(" agent-id "," message-content ")" |
"update_memory(" concept "," new-value ")" |
"allocate_attention(" amount "," cognitive-function ")"
concept ::= [a-zA-Z][a-zA-Z0-9_]*
truth-value ::= [0-9]+ "." [0-9]+
confidence-value ::= [0-9]+ "." [0-9]+
priority-value ::= [0-9]+ "." [0-9]+
threshold ::= [0-9]+ "." [0-9]+
agent-id ::= [0-9]+
amount ::= [0-9]+ "." [0-9]+
embedding-ref ::= "embedding_" [0-9]+
tensor-ref ::= "tensor_" [0-9]+
norm-type ::= "l1" | "l2" | "inf"
cognitive-function ::= "memory" | "reasoning" | "communication" | "attention"
objective ::= [a-zA-Z][a-zA-Z0-9_\s]*
message-content ::= "\"" [^"]* "\""
ws ::= [ \t\n]*
# PLN-style reasoning grammar
root ::= reasoning-pattern
reasoning-pattern ::=
deduction-rule |
induction-rule |
abduction-rule |
analogy-rule
deduction-rule ::=
"deduction(" ws
"premise1(" logical-statement ")" ws
"premise2(" logical-statement ")" ws
"conclusion(" logical-statement ")" ws
"strength(" strength-value ")" ws
")"
induction-rule ::=
"induction(" ws
"observations(" observation-list ")" ws
"pattern(" pattern-description ")" ws
"generalization(" logical-statement ")" ws
"confidence(" confidence-value ")" ws
")"
abduction-rule ::=
"abduction(" ws
"observation(" logical-statement ")" ws
"hypothesis(" logical-statement ")" ws
"plausibility(" plausibility-value ")" ws
")"
analogy-rule ::=
"analogy(" ws
"source_domain(" domain-description ")" ws
"target_domain(" domain-description ")" ws
"mapping(" mapping-list ")" ws
"inference(" logical-statement ")" ws
")"
observation-list ::= logical-statement (ws "," ws logical-statement)*
pattern-description ::= "\"" [^"]* "\""
domain-description ::= "\"" [^"]* "\""
mapping-list ::= mapping (ws "," ws mapping)*
mapping ::= concept "->" concept
strength-value ::= [0-9]+ "." [0-9]+
plausibility-value ::= [0-9]+ "." [0-9]+
logical-statement ::=
"belief(" concept "," truth-value "," confidence-value ")" |
"relation(" concept "," relation-type "," concept "," strength-value ")" |
"property(" concept "," property-name "," property-value ")"
relation-type ::= "is_a" | "part_of" | "causes" | "similar_to" | "implies"
property-name ::= [a-zA-Z][a-zA-Z0-9_]*
property-value ::= [a-zA-Z0-9_]+ | [0-9]+ "." [0-9]+
# Attention economy grammar
root ::= attention-command
attention-command ::=
allocate-attention |
reallocate-attention |
attention-query
allocate-attention ::=
"allocate(" ws
"amount(" attention-amount ")" ws
"target(" attention-target ")" ws
"priority(" priority-level ")" ws
"duration(" time-duration ")" ws
")"
reallocate-attention ::=
"reallocate(" ws
"from(" attention-source ")" ws
"to(" attention-target ")" ws
"amount(" attention-amount ")" ws
"reason(" reallocation-reason ")" ws
")"
attention-query ::=
"query_attention(" ws
"target(" attention-target ")" ws
"metric(" attention-metric ")" ws
")"
attention-target ::=
cognitive-function |
specific-task |
agent-communication
attention-source ::= attention-target
cognitive-function ::= "memory" | "reasoning" | "planning" | "communication"
specific-task ::=
"task(" task-identifier ")" |
"goal(" goal-identifier ")"
agent-communication ::=
"agent(" agent-id ")" |
"message_type(" message-type ")"
attention-amount ::= [0-9]+ "." [0-9]+
priority-level ::= "critical" | "high" | "medium" | "low"
time-duration ::= [0-9]+ time-unit
time-unit ::= "ms" | "s" | "min" | "hour"
reallocation-reason ::= "performance_drop" | "new_priority" | "resource_constraint"
attention-metric ::= "current_allocation" | "utilization" | "efficiency"
task-identifier ::= [a-zA-Z_][a-zA-Z0-9_]*
goal-identifier ::= [a-zA-Z_][a-zA-Z0-9_]*
message-type ::= "query" | "response" | "broadcast" | "update"
task(solve_consciousness_question)
preconditions(
knowledge(consciousness, embedding_1),
knowledge(neural_networks, embedding_2),
tensor_similarity(tensor_1, tensor_2, 0.7)
)
decomposition(
task(gather_definitions),
task(analyze_perspectives),
task(synthesize_answer)
)
postconditions(
belief(consciousness_understood, 0.8, 0.7),
goal(share_knowledge, 0.9)
)
task(collaborative_reasoning)
preconditions(
belief(problem_complex, 0.9, 0.8),
knowledge(other_agents, embedding_3)
)
decomposition(
send_message(agent_2, "Request expertise on consciousness"),
task(local_analysis),
task(integrate_responses),
update_memory(collaboration_history, new_entry)
)
postconditions(
belief(solution_quality_improved, 0.85, 0.9)
)
allocate(
amount(0.4),
target(memory),
priority(high),
duration(5000ms)
)
reallocate(
from(task(routine_processing)),
to(agent(urgent_communication)),
amount(0.2),
reason(new_priority)
)
deduction(
premise1(belief(humans_conscious, 0.9, 0.95)),
premise2(relation(consciousness, requires, self_awareness, 0.8)),
conclusion(belief(humans_self_aware, 0.8, 0.9)),
strength(0.85)
)
analogy(
source_domain("human consciousness"),
target_domain("artificial intelligence"),
mapping(
neural_activity -> computational_process,
self_awareness -> recursive_self_modeling,
consciousness -> emergent_intelligence
),
inference(belief(ai_can_be_conscious, 0.6, 0.7))
)
#include "llama-grammar.h"
// Cognitive grammar parser
struct cognitive_grammar_parser {
struct llama_grammar* grammar;
char* grammar_rules;
// Parsing state
cognitive_parse_state state;
parsed_cognitive_command* current_command;
};
// Parse cognitive grammar command
parsed_cognitive_command* parse_cognitive_command(
cognitive_grammar_parser* parser,
const char* input) {
// Use llama.cpp grammar system
struct llama_grammar* grammar = llama_grammar_init_impl(
parser->grammar_rules, "root");
// Parse input according to cognitive grammar
bool parse_success = llama_grammar_accept(grammar, input, strlen(input));
if (!parse_success) {
return NULL;
}
// Extract structured command
parsed_cognitive_command* cmd = extract_command_structure(parser, input);
llama_grammar_free_impl(grammar);
return cmd;
}
// Execute parsed cognitive command
void execute_cognitive_command(cognitive_agent* agent,
parsed_cognitive_command* cmd) {
switch (cmd->type) {
case COGNITIVE_CMD_TASK:
execute_task_command(agent, &cmd->task);
break;
case COGNITIVE_CMD_REASONING:
execute_reasoning_command(agent, &cmd->reasoning);
break;
case COGNITIVE_CMD_ATTENTION:
execute_attention_command(agent, &cmd->attention);
break;
}
}// Execute task decomposition
void execute_task_command(cognitive_agent* agent, parsed_task_command* task) {
// Check preconditions
if (!check_preconditions(agent, task->preconditions)) {
defer_task(agent, task);
return;
}
// Execute subtasks
for (size_t i = 0; i < task->subtask_count; i++) {
subtask* sub = &task->subtasks[i];
if (sub->type == SUBTASK_PRIMITIVE) {
execute_primitive_action(agent, &sub->action);
} else if (sub->type == SUBTASK_COMPLEX) {
// Recursive task execution
execute_task_command(agent, &sub->task);
}
}
// Verify postconditions
verify_postconditions(agent, task->postconditions);
}
// Execute primitive actions
void execute_primitive_action(cognitive_agent* agent, primitive_action* action) {
switch (action->type) {
case ACTION_SEND_MESSAGE:
send_cognitive_tensor(agent, action->target_agent,
action->message_tensor, action->attention_weight);
break;
case ACTION_UPDATE_MEMORY:
update_hypergraph_memory(agent->memory, action->concept,
action->new_value);
break;
case ACTION_ALLOCATE_ATTENTION:
allocate_attention(agent->attention, action->amount,
action->target_function);
break;
}
}// Generate cognitive response using grammar constraints
char* generate_cognitive_response(cognitive_agent* agent,
cognitive_rpc_message* input_msg) {
// Determine appropriate response grammar
const char* response_grammar = select_response_grammar(input_msg);
// Use constrained generation (similar to llama.cpp grammar)
struct llama_grammar* grammar = llama_grammar_init_impl(
response_grammar, "root");
// Generate response with grammar constraints
char* response = generate_with_grammar_constraints(
agent->reasoning->language_model,
input_msg->context,
grammar
);
llama_grammar_free_impl(grammar);
return response;
}void test_cognitive_grammars() {
// Test task decomposition grammar
const char* valid_task =
"task(solve_problem) "
"preconditions(knowledge(math, embedding_1)) "
"decomposition(task(analyze), task(synthesize)) "
"postconditions(belief(solved, 0.9, 0.8))";
cognitive_grammar_parser* parser = init_grammar_parser();
parsed_cognitive_command* cmd = parse_cognitive_command(parser, valid_task);
assert(cmd != NULL);
assert(cmd->type == COGNITIVE_CMD_TASK);
assert(cmd->task.subtask_count == 2);
cleanup_grammar_parser(parser);
// Test reasoning grammar
const char* valid_reasoning =
"deduction("
"premise1(belief(humans_mortal, 0.99, 0.95)) "
"premise2(belief(socrates_human, 0.95, 0.9)) "
"conclusion(belief(socrates_mortal, 0.94, 0.85)) "
"strength(0.89)"
")";
cmd = parse_cognitive_command(parser, valid_reasoning);
assert(cmd != NULL);
assert(cmd->type == COGNITIVE_CMD_REASONING);
}void test_grammar_execution() {
cognitive_agent* agent = create_cognitive_agent("localhost:8000");
// Parse and execute a complete cognitive task
const char* complex_task =
"task(collaborative_analysis) "
"preconditions(knowledge(domain_expertise, embedding_5)) "
"decomposition("
"send_message(agent_2, \"Request collaboration\"), "
"allocate_attention(0.6, reasoning), "
"task(local_processing)"
") "
"postconditions(belief(analysis_complete, 0.8, 0.9))";
cognitive_grammar_parser* parser = init_grammar_parser();
parsed_cognitive_command* cmd = parse_cognitive_command(parser, complex_task);
// Execute the command
execute_cognitive_command(agent, cmd);
// Verify execution results
assert(agent->attention->reasoning_allocation >= 0.6);
// Additional assertions...
cleanup_cognitive_agent(agent);
cleanup_grammar_parser(parser);
}This cognitive grammar system provides a structured way to express and execute complex cognitive behaviors while leveraging the existing GBNF infrastructure from llama.cpp. The grammars ensure that cognitive operations are syntactically valid and semantically meaningful within the distributed agent network.