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feat: Phase 5.3 - Multi-Agent Learning Infrastructure Implement intelligent agent learning from Knowledge Graph execution history with per-task-type expertise tracking, recency bias, and learning curves. ## Phase 5.3 Implementation ### Learning Infrastructure (✅ Complete) - LearningProfileService with per-task-type expertise metrics - TaskTypeExpertise model tracking success_rate, confidence, learning curves - Recency bias weighting: recent 7 days weighted 3x higher (exponential decay) - Confidence scoring prevents overfitting: min(1.0, executions / 20) - Learning curves computed from daily execution windows ### Agent Scoring Service (✅ Complete) - Unified AgentScore combining SwarmCoordinator + learning profiles - Scoring formula: 0.3*base + 0.5*expertise + 0.2*confidence - Rank agents by combined score for intelligent assignment - Support for recency-biased scoring (recent_success_rate) - Methods: rank_agents, select_best, rank_agents_with_recency ### KG Integration (✅ Complete) - KGPersistence::get_executions_for_task_type() - query by agent + task type - KGPersistence::get_agent_executions() - all executions for agent - Coordinator::load_learning_profile_from_kg() - core KG→Learning integration - Coordinator::load_all_learning_profiles() - batch load for multiple agents - Convert PersistedExecution → ExecutionData for learning calculations ### Agent Assignment Integration (✅ Complete) - AgentCoordinator uses learning profiles for task assignment - extract_task_type() infers task type from title/description - assign_task() scores candidates using AgentScoringService - Fallback to load-based selection if no learning data available - Learning profiles stored in coordinator.learning_profiles RwLock ### Profile Adapter Enhancements (✅ Complete) - create_learning_profile() - initialize empty profiles - add_task_type_expertise() - set task-type expertise - update_profile_with_learning() - update swarm profiles from learning ## Files Modified ### vapora-knowledge-graph/src/persistence.rs (+30 lines) - get_executions_for_task_type(agent_id, task_type, limit) - get_agent_executions(agent_id, limit) ### vapora-agents/src/coordinator.rs (+100 lines) - load_learning_profile_from_kg() - core KG integration method - load_all_learning_profiles() - batch loading for agents - assign_task() already uses learning-based scoring via AgentScoringService ### Existing Complete Implementation - vapora-knowledge-graph/src/learning.rs - calculation functions - vapora-agents/src/learning_profile.rs - data structures and expertise - vapora-agents/src/scoring.rs - unified scoring service - vapora-agents/src/profile_adapter.rs - adapter methods ## Tests Passing - learning_profile: 7 tests ✅ - scoring: 5 tests ✅ - profile_adapter: 6 tests ✅ - coordinator: learning-specific tests ✅ ## Data Flow 1. Task arrives → AgentCoordinator::assign_task() 2. Extract task_type from description 3. Query KG for task-type executions (load_learning_profile_from_kg) 4. Calculate expertise with recency bias 5. Score candidates (SwarmCoordinator + learning) 6. Assign to top-scored agent 7. Execution result → KG → Update learning profiles ## Key Design Decisions ✅ Recency bias: 7-day half-life with 3x weight for recent performance ✅ Confidence scoring: min(1.0, total_executions / 20) prevents overfitting ✅ Hierarchical scoring: 30% base load, 50% expertise, 20% confidence ✅ KG query limit: 100 recent executions per task-type for performance ✅ Async loading: load_learning_profile_from_kg supports concurrent loads ## Next: Phase 5.4 - Cost Optimization Ready to implement budget enforcement and cost-aware provider selection.
2026-01-11 13:03:53 +00:00
use chrono::{DateTime, Utc};
use serde::{Deserialize, Serialize};
/// Message type for agent-to-agent communication
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum SwarmMessage {
TaskProposal {
task_id: String,
proposed_by: String,
task_description: String,
required_capabilities: Vec<String>,
},
BidRequest {
task_id: String,
task_description: String,
},
BidSubmission {
task_id: String,
bidder_id: String,
bid_value: f64,
estimated_duration_ms: u64,
},
TaskAssignment {
task_id: String,
assigned_to: String,
priority: u32,
},
ConsensusVote {
proposal_id: String,
voter_id: String,
vote: Vote,
reasoning: String,
},
CoalitionInvite {
coalition_id: String,
coordinator_id: String,
required_roles: Vec<String>,
},
CoalitionAccept {
coalition_id: String,
agent_id: String,
},
StatusUpdate {
agent_id: String,
current_load: f64,
available: bool,
},
}
/// Vote in consensus mechanism
#[derive(Debug, Clone, Copy, Serialize, Deserialize, PartialEq, Eq)]
pub enum Vote {
Agree,
Disagree,
Abstain,
}
/// Bid for task execution
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Bid {
pub task_id: String,
pub bidder_id: String,
pub bid_value: f64,
pub estimated_duration_ms: u64,
pub submitted_at: DateTime<Utc>,
}
/// Coalition of agents working together
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Coalition {
pub id: String,
pub coordinator_id: String,
pub members: Vec<String>,
pub required_roles: Vec<String>,
pub status: CoalitionStatus,
pub created_at: DateTime<Utc>,
}
/// Coalition status
#[derive(Debug, Clone, Copy, Serialize, Deserialize, PartialEq, Eq)]
pub enum CoalitionStatus {
Forming,
Active,
Executing,
Completed,
Failed,
}
/// Agent capability profile in swarm
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AgentProfile {
pub id: String,
pub roles: Vec<String>,
pub capabilities: Vec<String>,
pub current_load: f64,
pub success_rate: f64,
pub availability: bool,
}
impl Bid {
pub fn new(task_id: String, bidder_id: String, bid_value: f64, duration_ms: u64) -> Self {
Self {
task_id,
bidder_id,
bid_value,
estimated_duration_ms: duration_ms,
submitted_at: Utc::now(),
}
}
}
impl Coalition {
pub fn new(
coordinator_id: String,
required_roles: Vec<String>,
) -> Self {
Self {
id: format!("coal_{}", uuid::Uuid::new_v4()),
coordinator_id,
members: Vec::new(),
required_roles,
status: CoalitionStatus::Forming,
created_at: Utc::now(),
}
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_bid_creation() {
let bid = Bid::new("task-1".to_string(), "agent-1".to_string(), 0.8, 5000);
assert_eq!(bid.task_id, "task-1");
assert_eq!(bid.bid_value, 0.8);
}
#[test]
fn test_coalition_creation() {
let coal = Coalition::new("agent-1".to_string(), vec!["developer".to_string()]);
assert_eq!(coal.coordinator_id, "agent-1");
assert_eq!(coal.status, CoalitionStatus::Forming);
}
}