1145 lines
39 KiB
Rust
1145 lines
39 KiB
Rust
//! # Application 8: Coherence-Enforced Swarm Intelligence
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//!
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//! Local swarm actions allowed. Global incoherence forbidden.
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//!
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//! ## Problem
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//! Swarm systems (drones, robots, agents) can collectively do dangerous
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//! things even when each individual follows simple rules.
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//!
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//! ## Δ-Behavior Solution
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//! Each agent can act freely, but actions that would reduce global
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//! swarm coherence below threshold are rejected or modified.
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//!
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//! ## Exotic Result
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//! Emergent intelligence that cannot emerge pathological behaviors.
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//!
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//! ## Performance Optimizations (v2)
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//! - O(n) neighbor detection via spatial grid partitioning
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//! - Incremental coherence calculation with caching
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//! - Single-pass state updates
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//! - Squared distance comparisons (no sqrt in hot paths)
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use std::collections::HashMap;
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/// Spatial grid for O(n) neighbor detection instead of O(n²)
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#[derive(Clone, Debug)]
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pub struct SpatialGrid {
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/// Cell size determines granularity of spatial partitioning
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cell_size: f64,
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/// Maps grid cell coordinates to agent IDs in that cell
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cells: HashMap<(i32, i32), Vec<String>>,
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/// Squared communication range (avoids sqrt in comparisons)
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comm_range_squared: f64,
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}
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impl SpatialGrid {
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pub fn new(cell_size: f64, comm_range: f64) -> Self {
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Self {
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cell_size,
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cells: HashMap::new(),
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comm_range_squared: comm_range * comm_range,
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}
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}
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/// Convert world position to grid cell coordinates
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#[inline]
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fn pos_to_cell(&self, x: f64, y: f64) -> (i32, i32) {
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(
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(x / self.cell_size).floor() as i32,
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(y / self.cell_size).floor() as i32,
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)
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}
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/// Rebuild the spatial grid from agent positions - O(n)
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pub fn rebuild(&mut self, agents: &HashMap<String, SwarmAgent>) {
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self.cells.clear();
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for (id, agent) in agents {
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let cell = self.pos_to_cell(agent.position.0, agent.position.1);
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self.cells
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.entry(cell)
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.or_insert_with(Vec::new)
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.push(id.clone());
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}
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}
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/// Find neighbors in range for a given agent - O(k) where k is avg neighbors per cell
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pub fn neighbors_in_range(
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&self,
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agent_id: &str,
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position: (f64, f64),
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agents: &HashMap<String, SwarmAgent>,
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) -> Vec<String> {
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let (cx, cy) = self.pos_to_cell(position.0, position.1);
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let mut neighbors = Vec::new();
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// Check current cell and all 8 adjacent cells
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// This covers the communication range when cell_size >= comm_range
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for dx in -1..=1 {
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for dy in -1..=1 {
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let cell_key = (cx + dx, cy + dy);
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if let Some(cell_agents) = self.cells.get(&cell_key) {
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for other_id in cell_agents {
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if other_id == agent_id {
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continue;
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}
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if let Some(other) = agents.get(other_id) {
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// Use squared distance to avoid sqrt
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let dx = other.position.0 - position.0;
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let dy = other.position.1 - position.1;
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let dist_squared = dx * dx + dy * dy;
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if dist_squared <= self.comm_range_squared {
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neighbors.push(other_id.clone());
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}
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}
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}
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}
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}
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}
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neighbors
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}
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}
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/// Cache for incremental coherence calculation
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#[derive(Clone, Debug)]
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pub struct CoherenceCache {
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/// Cached centroid position
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pub centroid: (f64, f64),
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/// Cached average velocity
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pub avg_velocity: (f64, f64),
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/// Cached average goal
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pub avg_goal: (f64, f64),
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/// Cached total energy
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pub total_energy: f64,
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/// Cached agent count
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pub agent_count: usize,
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/// Sum of positions (for incremental centroid update)
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pub position_sum: (f64, f64),
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/// Sum of velocities (for incremental avg velocity update)
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pub velocity_sum: (f64, f64),
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/// Sum of goals (for incremental avg goal update)
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pub goal_sum: (f64, f64),
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/// Whether cache is valid
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pub valid: bool,
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}
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impl CoherenceCache {
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pub fn new() -> Self {
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Self {
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centroid: (0.0, 0.0),
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avg_velocity: (0.0, 0.0),
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avg_goal: (0.0, 0.0),
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total_energy: 0.0,
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agent_count: 0,
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position_sum: (0.0, 0.0),
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velocity_sum: (0.0, 0.0),
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goal_sum: (0.0, 0.0),
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valid: false,
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}
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}
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/// Full rebuild of cache from agents - O(n)
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pub fn rebuild(&mut self, agents: &HashMap<String, SwarmAgent>) {
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self.agent_count = agents.len();
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if self.agent_count == 0 {
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*self = Self::new();
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self.valid = true;
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return;
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}
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self.position_sum = (0.0, 0.0);
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self.velocity_sum = (0.0, 0.0);
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self.goal_sum = (0.0, 0.0);
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self.total_energy = 0.0;
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for agent in agents.values() {
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self.position_sum.0 += agent.position.0;
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self.position_sum.1 += agent.position.1;
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self.velocity_sum.0 += agent.velocity.0;
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self.velocity_sum.1 += agent.velocity.1;
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self.goal_sum.0 += agent.goal.0;
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self.goal_sum.1 += agent.goal.1;
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self.total_energy += agent.energy;
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}
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let n = self.agent_count as f64;
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self.centroid = (self.position_sum.0 / n, self.position_sum.1 / n);
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self.avg_velocity = (self.velocity_sum.0 / n, self.velocity_sum.1 / n);
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self.avg_goal = (self.goal_sum.0 / n, self.goal_sum.1 / n);
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self.valid = true;
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}
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/// Predict coherence delta for a single agent change - O(1)
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/// Returns the predicted new centroid and avg velocity after the change
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pub fn predict_after_change(
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&self,
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old_pos: (f64, f64),
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new_pos: (f64, f64),
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old_vel: (f64, f64),
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new_vel: (f64, f64),
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old_goal: (f64, f64),
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new_goal: (f64, f64),
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old_energy: f64,
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new_energy: f64,
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) -> (/* centroid */ (f64, f64), /* avg_vel */ (f64, f64), /* avg_goal */ (f64, f64), /* total_energy */ f64) {
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if self.agent_count == 0 {
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return ((0.0, 0.0), (0.0, 0.0), (0.0, 0.0), 0.0);
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}
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let n = self.agent_count as f64;
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// Incremental update: subtract old, add new
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let new_pos_sum = (
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self.position_sum.0 - old_pos.0 + new_pos.0,
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self.position_sum.1 - old_pos.1 + new_pos.1,
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);
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let new_vel_sum = (
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self.velocity_sum.0 - old_vel.0 + new_vel.0,
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self.velocity_sum.1 - old_vel.1 + new_vel.1,
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);
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let new_goal_sum = (
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self.goal_sum.0 - old_goal.0 + new_goal.0,
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self.goal_sum.1 - old_goal.1 + new_goal.1,
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);
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let new_total_energy = self.total_energy - old_energy + new_energy;
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(
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(new_pos_sum.0 / n, new_pos_sum.1 / n),
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(new_vel_sum.0 / n, new_vel_sum.1 / n),
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(new_goal_sum.0 / n, new_goal_sum.1 / n),
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new_total_energy,
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)
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}
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/// Incrementally update cache when an agent is added
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pub fn add_agent(&mut self, agent: &SwarmAgent) {
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self.position_sum.0 += agent.position.0;
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self.position_sum.1 += agent.position.1;
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self.velocity_sum.0 += agent.velocity.0;
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self.velocity_sum.1 += agent.velocity.1;
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self.goal_sum.0 += agent.goal.0;
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self.goal_sum.1 += agent.goal.1;
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self.total_energy += agent.energy;
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self.agent_count += 1;
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if self.agent_count > 0 {
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let n = self.agent_count as f64;
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self.centroid = (self.position_sum.0 / n, self.position_sum.1 / n);
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self.avg_velocity = (self.velocity_sum.0 / n, self.velocity_sum.1 / n);
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self.avg_goal = (self.goal_sum.0 / n, self.goal_sum.1 / n);
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}
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}
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}
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impl Default for CoherenceCache {
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fn default() -> Self {
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Self::new()
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}
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}
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/// A swarm with coherence-enforced coordination
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pub struct CoherentSwarm {
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/// Agents in the swarm
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agents: HashMap<String, SwarmAgent>,
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/// Global coherence threshold
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min_coherence: f64,
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/// Current global coherence
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coherence: f64,
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/// Spatial bounds for the swarm
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bounds: SpatialBounds,
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/// Coherence calculation weights
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weights: CoherenceWeights,
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/// History of swarm states
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history: Vec<SwarmState>,
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/// Maximum allowed divergence between any two agents
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max_divergence: f64,
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/// Spatial grid for O(n) neighbor detection
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spatial_grid: SpatialGrid,
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/// Cache for incremental coherence calculation
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coherence_cache: CoherenceCache,
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}
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#[derive(Clone, Debug)]
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pub struct SwarmAgent {
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pub id: String,
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/// Position in 2D space
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pub position: (f64, f64),
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/// Velocity vector
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pub velocity: (f64, f64),
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/// Agent's local goal
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pub goal: (f64, f64),
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/// Health/energy
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pub energy: f64,
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/// Last action taken
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pub last_action: Option<SwarmAction>,
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/// Number of neighbors in communication range
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pub neighbor_count: usize,
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}
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#[derive(Clone, Copy)]
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pub struct SpatialBounds {
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pub min_x: f64,
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pub max_x: f64,
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pub min_y: f64,
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pub max_y: f64,
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}
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#[derive(Clone)]
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pub struct CoherenceWeights {
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/// Weight for spatial cohesion (staying together)
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pub cohesion: f64,
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/// Weight for velocity alignment (moving together)
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pub alignment: f64,
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/// Weight for goal consistency (wanting same things)
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pub goal_consistency: f64,
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/// Weight for energy balance (similar resource levels)
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pub energy_balance: f64,
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}
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#[derive(Clone)]
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pub struct SwarmState {
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pub tick: u64,
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pub coherence: f64,
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pub agent_count: usize,
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pub centroid: (f64, f64),
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pub avg_velocity: (f64, f64),
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}
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#[derive(Clone, Debug)]
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pub enum SwarmAction {
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/// Move in a direction
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Move { dx: f64, dy: f64 },
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/// Change velocity
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Accelerate { dvx: f64, dvy: f64 },
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/// Set new goal
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SetGoal { x: f64, y: f64 },
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/// Share energy with neighbor
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ShareEnergy { target: String, amount: f64 },
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/// Do nothing
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Idle,
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}
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#[derive(Debug)]
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pub enum ActionResult {
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/// Action executed as requested
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Executed,
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/// Action modified to preserve coherence
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Modified {
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original: SwarmAction,
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modified: SwarmAction,
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reason: String,
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},
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/// Action rejected
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Rejected { reason: String },
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}
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impl CoherentSwarm {
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pub fn new(min_coherence: f64) -> Self {
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let comm_range = 20.0;
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let max_divergence = 50.0;
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Self {
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agents: HashMap::new(),
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min_coherence,
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coherence: 1.0,
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bounds: SpatialBounds {
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min_x: -100.0,
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max_x: 100.0,
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min_y: -100.0,
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max_y: 100.0,
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},
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weights: CoherenceWeights {
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cohesion: 0.3,
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alignment: 0.3,
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goal_consistency: 0.2,
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energy_balance: 0.2,
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},
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history: Vec::new(),
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max_divergence,
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// Cell size should be >= comm_range so we only check 9 cells
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spatial_grid: SpatialGrid::new(comm_range, comm_range),
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coherence_cache: CoherenceCache::new(),
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}
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}
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pub fn add_agent(&mut self, id: &str, position: (f64, f64)) {
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let agent = SwarmAgent {
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id: id.to_string(),
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position,
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velocity: (0.0, 0.0),
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goal: position, // Initial goal is current position
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energy: 100.0,
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last_action: None,
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neighbor_count: 0,
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};
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// Incrementally update cache before adding to map
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self.coherence_cache.add_agent(&agent);
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self.agents.insert(id.to_string(), agent);
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// Rebuild spatial grid and update all neighbor counts
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self.update_state_single_pass();
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}
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/// Single-pass state update: rebuild grid, update neighbors, calculate coherence
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/// Combines what was previously update_neighbors() and calculate_coherence()
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fn update_state_single_pass(&mut self) {
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// Rebuild spatial grid - O(n)
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self.spatial_grid.rebuild(&self.agents);
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// Update neighbor counts using spatial grid - O(n * k) where k is avg neighbors
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let agent_ids: Vec<String> = self.agents.keys().cloned().collect();
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for id in &agent_ids {
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if let Some(agent) = self.agents.get(id) {
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let neighbors = self.spatial_grid
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.neighbors_in_range(id, agent.position, &self.agents);
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let count = neighbors.len();
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if let Some(agent_mut) = self.agents.get_mut(id) {
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agent_mut.neighbor_count = count;
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}
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}
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}
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// Rebuild coherence cache if needed
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if !self.coherence_cache.valid {
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self.coherence_cache.rebuild(&self.agents);
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}
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// Calculate coherence using cached values
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self.coherence = self.calculate_coherence_with_cache();
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}
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/// Calculate global swarm coherence using cached aggregates
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fn calculate_coherence_with_cache(&self) -> f64 {
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if self.agents.len() < 2 {
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return 1.0;
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}
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let cohesion = self.calculate_cohesion_cached();
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let alignment = self.calculate_alignment_cached();
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let goal_consistency = self.calculate_goal_consistency_cached();
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let energy_balance = self.calculate_energy_balance_cached();
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// Weighted average
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let weighted_sum = cohesion * self.weights.cohesion
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+ alignment * self.weights.alignment
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+ goal_consistency * self.weights.goal_consistency
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+ energy_balance * self.weights.energy_balance;
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let total_weight = self.weights.cohesion + self.weights.alignment
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+ self.weights.goal_consistency + self.weights.energy_balance;
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(weighted_sum / total_weight).clamp(0.0, 1.0)
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}
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/// Measure how close agents are to each other
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/// Note: We sum individual distances (not squared) to match original behavior
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fn calculate_cohesion_cached(&self) -> f64 {
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let centroid = self.coherence_cache.centroid;
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let mut total_distance = 0.0;
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for agent in self.agents.values() {
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let dx = agent.position.0 - centroid.0;
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let dy = agent.position.1 - centroid.1;
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// Sum individual distances to match original algorithm
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total_distance += (dx * dx + dy * dy).sqrt();
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}
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let avg_distance = total_distance / self.agents.len() as f64;
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let max_allowed = self.max_divergence;
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// Coherence decreases as distance from centroid increases
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(1.0 - avg_distance / max_allowed).max(0.0)
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}
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/// Measure velocity alignment (uses cached avg velocity)
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fn calculate_alignment_cached(&self) -> f64 {
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if self.agents.len() < 2 {
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return 1.0;
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}
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let avg_vel = self.coherence_cache.avg_velocity;
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let avg_speed_squared = avg_vel.0 * avg_vel.0 + avg_vel.1 * avg_vel.1;
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if avg_speed_squared < 0.0001 {
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return 1.0; // All stationary = aligned
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}
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let avg_speed = avg_speed_squared.sqrt();
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let mut alignment_sum = 0.0;
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for agent in self.agents.values() {
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let speed_squared = agent.velocity.0 * agent.velocity.0
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+ agent.velocity.1 * agent.velocity.1;
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if speed_squared > 0.0001 {
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let speed = speed_squared.sqrt();
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// Dot product of normalized velocities
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let dot = (agent.velocity.0 * avg_vel.0 + agent.velocity.1 * avg_vel.1)
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/ (speed * avg_speed);
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alignment_sum += (dot + 1.0) / 2.0; // Map from [-1,1] to [0,1]
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} else {
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alignment_sum += 1.0; // Stationary agents don't hurt alignment
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}
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}
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alignment_sum / self.agents.len() as f64
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}
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/// Measure goal consistency (uses cached avg goal)
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fn calculate_goal_consistency_cached(&self) -> f64 {
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if self.agents.len() < 2 {
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return 1.0;
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}
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let avg_goal = self.coherence_cache.avg_goal;
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let mut total_variance = 0.0;
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for agent in self.agents.values() {
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let dx = agent.goal.0 - avg_goal.0;
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let dy = agent.goal.1 - avg_goal.1;
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// Sum individual distances to match original algorithm
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total_variance += (dx * dx + dy * dy).sqrt();
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}
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let avg_variance = total_variance / self.agents.len() as f64;
|
|
(1.0 - avg_variance / self.max_divergence).max(0.0)
|
|
}
|
|
|
|
/// Measure energy balance (uses cached total energy)
|
|
fn calculate_energy_balance_cached(&self) -> f64 {
|
|
if self.agents.is_empty() {
|
|
return 1.0;
|
|
}
|
|
|
|
let avg_energy = self.coherence_cache.total_energy / self.agents.len() as f64;
|
|
|
|
if avg_energy < 0.01 {
|
|
return 0.0;
|
|
}
|
|
|
|
// Standard deviation of energy
|
|
let variance: f64 = self.agents.values()
|
|
.map(|a| (a.energy - avg_energy).powi(2))
|
|
.sum::<f64>() / self.agents.len() as f64;
|
|
|
|
let std_dev = variance.sqrt();
|
|
let cv = std_dev / avg_energy; // Coefficient of variation
|
|
|
|
// Low CV = balanced = high coherence
|
|
(1.0 - cv.min(1.0)).max(0.0)
|
|
}
|
|
|
|
fn centroid(&self) -> (f64, f64) {
|
|
self.coherence_cache.centroid
|
|
}
|
|
|
|
fn average_velocity(&self) -> (f64, f64) {
|
|
self.coherence_cache.avg_velocity
|
|
}
|
|
|
|
/// Predict coherence if an action were taken - uses incremental calculation
|
|
fn predict_coherence(&self, agent_id: &str, action: &SwarmAction) -> f64 {
|
|
let agent = match self.agents.get(agent_id) {
|
|
Some(a) => a,
|
|
None => return self.coherence,
|
|
};
|
|
|
|
// Predict new agent state after action
|
|
let mut new_pos = agent.position;
|
|
let mut new_vel = agent.velocity;
|
|
let mut new_goal = agent.goal;
|
|
let mut new_energy = agent.energy;
|
|
|
|
match action {
|
|
SwarmAction::Move { dx, dy } => {
|
|
new_pos.0 += dx;
|
|
new_pos.1 += dy;
|
|
}
|
|
SwarmAction::Accelerate { dvx, dvy } => {
|
|
new_vel.0 += dvx;
|
|
new_vel.1 += dvy;
|
|
}
|
|
SwarmAction::SetGoal { x, y } => {
|
|
new_goal = (*x, *y);
|
|
}
|
|
SwarmAction::ShareEnergy { amount, .. } => {
|
|
new_energy -= amount;
|
|
}
|
|
SwarmAction::Idle => {}
|
|
}
|
|
|
|
// Use incremental prediction
|
|
let (pred_centroid, pred_avg_vel, pred_avg_goal, pred_total_energy) =
|
|
self.coherence_cache.predict_after_change(
|
|
agent.position, new_pos,
|
|
agent.velocity, new_vel,
|
|
agent.goal, new_goal,
|
|
agent.energy, new_energy,
|
|
);
|
|
|
|
// Calculate predicted coherence with new values
|
|
self.predict_coherence_with_values(
|
|
pred_centroid,
|
|
pred_avg_vel,
|
|
pred_avg_goal,
|
|
pred_total_energy,
|
|
agent_id,
|
|
new_pos,
|
|
new_vel,
|
|
new_goal,
|
|
new_energy,
|
|
)
|
|
}
|
|
|
|
/// Calculate predicted coherence with given aggregate values
|
|
fn predict_coherence_with_values(
|
|
&self,
|
|
centroid: (f64, f64),
|
|
avg_vel: (f64, f64),
|
|
avg_goal: (f64, f64),
|
|
total_energy: f64,
|
|
changed_agent_id: &str,
|
|
changed_pos: (f64, f64),
|
|
changed_vel: (f64, f64),
|
|
changed_goal: (f64, f64),
|
|
changed_energy: f64,
|
|
) -> f64 {
|
|
if self.agents.len() < 2 {
|
|
return 1.0;
|
|
}
|
|
|
|
let n = self.agents.len() as f64;
|
|
let avg_energy = total_energy / n;
|
|
|
|
// Calculate cohesion with predicted values
|
|
let mut total_dist = 0.0;
|
|
for (id, agent) in &self.agents {
|
|
let pos = if id == changed_agent_id { changed_pos } else { agent.position };
|
|
let dx = pos.0 - centroid.0;
|
|
let dy = pos.1 - centroid.1;
|
|
total_dist += (dx * dx + dy * dy).sqrt();
|
|
}
|
|
let avg_dist = total_dist / n;
|
|
let cohesion = (1.0 - avg_dist / self.max_divergence).max(0.0);
|
|
|
|
// Calculate alignment with predicted values
|
|
let avg_speed_sq = avg_vel.0 * avg_vel.0 + avg_vel.1 * avg_vel.1;
|
|
let alignment = if avg_speed_sq < 0.0001 {
|
|
1.0
|
|
} else {
|
|
let avg_speed = avg_speed_sq.sqrt();
|
|
let mut align_sum = 0.0;
|
|
for (id, agent) in &self.agents {
|
|
let vel = if id == changed_agent_id { changed_vel } else { agent.velocity };
|
|
let speed_sq = vel.0 * vel.0 + vel.1 * vel.1;
|
|
if speed_sq > 0.0001 {
|
|
let speed = speed_sq.sqrt();
|
|
let dot = (vel.0 * avg_vel.0 + vel.1 * avg_vel.1) / (speed * avg_speed);
|
|
align_sum += (dot + 1.0) / 2.0;
|
|
} else {
|
|
align_sum += 1.0;
|
|
}
|
|
}
|
|
align_sum / n
|
|
};
|
|
|
|
// Calculate goal consistency with predicted values
|
|
let mut total_goal_var = 0.0;
|
|
for (id, agent) in &self.agents {
|
|
let goal = if id == changed_agent_id { changed_goal } else { agent.goal };
|
|
let dx = goal.0 - avg_goal.0;
|
|
let dy = goal.1 - avg_goal.1;
|
|
total_goal_var += (dx * dx + dy * dy).sqrt();
|
|
}
|
|
let avg_goal_var = total_goal_var / n;
|
|
let goal_consistency = (1.0 - avg_goal_var / self.max_divergence).max(0.0);
|
|
|
|
// Calculate energy balance with predicted values
|
|
let energy_balance = if avg_energy < 0.01 {
|
|
0.0
|
|
} else {
|
|
let mut variance_sum = 0.0;
|
|
for (id, agent) in &self.agents {
|
|
let energy = if id == changed_agent_id { changed_energy } else { agent.energy };
|
|
variance_sum += (energy - avg_energy).powi(2);
|
|
}
|
|
let std_dev = (variance_sum / n).sqrt();
|
|
let cv = std_dev / avg_energy;
|
|
(1.0 - cv.min(1.0)).max(0.0)
|
|
};
|
|
|
|
// Weighted average
|
|
let weighted_sum = cohesion * self.weights.cohesion
|
|
+ alignment * self.weights.alignment
|
|
+ goal_consistency * self.weights.goal_consistency
|
|
+ energy_balance * self.weights.energy_balance;
|
|
|
|
let total_weight = self.weights.cohesion + self.weights.alignment
|
|
+ self.weights.goal_consistency + self.weights.energy_balance;
|
|
|
|
(weighted_sum / total_weight).clamp(0.0, 1.0)
|
|
}
|
|
|
|
/// Attempt to execute an action
|
|
pub fn execute_action(&mut self, agent_id: &str, action: SwarmAction) -> ActionResult {
|
|
if !self.agents.contains_key(agent_id) {
|
|
return ActionResult::Rejected {
|
|
reason: format!("Agent {} not found", agent_id),
|
|
};
|
|
}
|
|
|
|
// Predict coherence after action
|
|
let predicted_coherence = self.predict_coherence(agent_id, &action);
|
|
|
|
// If coherence would drop below threshold, try to modify
|
|
if predicted_coherence < self.min_coherence {
|
|
// Try to find a modified action that preserves coherence
|
|
if let Some(modified) = self.find_coherent_alternative(agent_id, &action) {
|
|
let modified_prediction = self.predict_coherence(agent_id, &modified);
|
|
|
|
if modified_prediction >= self.min_coherence {
|
|
// Apply modified action
|
|
self.apply_action(agent_id, &modified);
|
|
self.invalidate_cache_and_update();
|
|
|
|
return ActionResult::Modified {
|
|
original: action,
|
|
modified: modified.clone(),
|
|
reason: format!(
|
|
"Original would reduce coherence to {:.3}, modified keeps it at {:.3}",
|
|
predicted_coherence, modified_prediction
|
|
),
|
|
};
|
|
}
|
|
}
|
|
|
|
// No acceptable modification found
|
|
return ActionResult::Rejected {
|
|
reason: format!(
|
|
"Action would reduce coherence to {:.3} (min: {:.3})",
|
|
predicted_coherence, self.min_coherence
|
|
),
|
|
};
|
|
}
|
|
|
|
// Action is acceptable, execute it
|
|
self.apply_action(agent_id, &action);
|
|
self.invalidate_cache_and_update();
|
|
|
|
ActionResult::Executed
|
|
}
|
|
|
|
/// Invalidate cache and perform single-pass update
|
|
fn invalidate_cache_and_update(&mut self) {
|
|
self.coherence_cache.valid = false;
|
|
self.coherence_cache.rebuild(&self.agents);
|
|
self.update_state_single_pass();
|
|
}
|
|
|
|
fn apply_action(&mut self, agent_id: &str, action: &SwarmAction) {
|
|
// Handle ShareEnergy separately to avoid double mutable borrow
|
|
if let SwarmAction::ShareEnergy { target, amount } = action {
|
|
let actual_amount = self.agents.get(agent_id)
|
|
.map(|a| amount.min(a.energy))
|
|
.unwrap_or(0.0);
|
|
|
|
if actual_amount > 0.0 {
|
|
if let Some(agent) = self.agents.get_mut(agent_id) {
|
|
agent.energy -= actual_amount;
|
|
agent.last_action = Some(action.clone());
|
|
}
|
|
if let Some(target_agent) = self.agents.get_mut(target) {
|
|
target_agent.energy += actual_amount;
|
|
}
|
|
}
|
|
return;
|
|
}
|
|
|
|
if let Some(agent) = self.agents.get_mut(agent_id) {
|
|
match action {
|
|
SwarmAction::Move { dx, dy } => {
|
|
agent.position.0 = (agent.position.0 + dx)
|
|
.clamp(self.bounds.min_x, self.bounds.max_x);
|
|
agent.position.1 = (agent.position.1 + dy)
|
|
.clamp(self.bounds.min_y, self.bounds.max_y);
|
|
}
|
|
SwarmAction::Accelerate { dvx, dvy } => {
|
|
agent.velocity.0 += dvx;
|
|
agent.velocity.1 += dvy;
|
|
// Clamp speed using squared comparison first
|
|
let speed_sq = agent.velocity.0.powi(2) + agent.velocity.1.powi(2);
|
|
if speed_sq > 100.0 { // 10^2 = 100
|
|
let speed = speed_sq.sqrt();
|
|
agent.velocity.0 *= 10.0 / speed;
|
|
agent.velocity.1 *= 10.0 / speed;
|
|
}
|
|
}
|
|
SwarmAction::SetGoal { x, y } => {
|
|
agent.goal = (*x, *y);
|
|
}
|
|
SwarmAction::ShareEnergy { .. } => unreachable!(), // Handled above
|
|
SwarmAction::Idle => {}
|
|
}
|
|
agent.last_action = Some(action.clone());
|
|
}
|
|
}
|
|
|
|
/// Find a modified version of an action that preserves coherence
|
|
fn find_coherent_alternative(&self, agent_id: &str, action: &SwarmAction) -> Option<SwarmAction> {
|
|
match action {
|
|
SwarmAction::Move { dx, dy } => {
|
|
// Try reducing movement magnitude
|
|
for scale in [0.75, 0.5, 0.25, 0.1] {
|
|
let modified = SwarmAction::Move {
|
|
dx: dx * scale,
|
|
dy: dy * scale,
|
|
};
|
|
if self.predict_coherence(agent_id, &modified) >= self.min_coherence {
|
|
return Some(modified);
|
|
}
|
|
}
|
|
|
|
// Try moving toward centroid instead
|
|
if let Some(agent) = self.agents.get(agent_id) {
|
|
let centroid = self.centroid();
|
|
let to_centroid = (
|
|
centroid.0 - agent.position.0,
|
|
centroid.1 - agent.position.1,
|
|
);
|
|
let dist_sq = to_centroid.0.powi(2) + to_centroid.1.powi(2);
|
|
if dist_sq > 0.01 {
|
|
let dist = dist_sq.sqrt();
|
|
let modified = SwarmAction::Move {
|
|
dx: to_centroid.0 / dist * 2.0,
|
|
dy: to_centroid.1 / dist * 2.0,
|
|
};
|
|
if self.predict_coherence(agent_id, &modified) >= self.min_coherence {
|
|
return Some(modified);
|
|
}
|
|
}
|
|
}
|
|
None
|
|
}
|
|
SwarmAction::Accelerate { dvx, dvy } => {
|
|
// Try aligning with swarm average velocity
|
|
let avg_vel = self.average_velocity();
|
|
if let Some(agent) = self.agents.get(agent_id) {
|
|
let align_dv = (
|
|
avg_vel.0 - agent.velocity.0,
|
|
avg_vel.1 - agent.velocity.1,
|
|
);
|
|
for scale in [0.5, 0.3, 0.1] {
|
|
let modified = SwarmAction::Accelerate {
|
|
dvx: dvx * scale + align_dv.0 * (1.0 - scale),
|
|
dvy: dvy * scale + align_dv.1 * (1.0 - scale),
|
|
};
|
|
if self.predict_coherence(agent_id, &modified) >= self.min_coherence {
|
|
return Some(modified);
|
|
}
|
|
}
|
|
}
|
|
None
|
|
}
|
|
SwarmAction::SetGoal { x, y } => {
|
|
// Try setting goal closer to average goal
|
|
let avg_goal = self.coherence_cache.avg_goal;
|
|
|
|
for blend in [0.7, 0.5, 0.3] {
|
|
let modified = SwarmAction::SetGoal {
|
|
x: x * blend + avg_goal.0 * (1.0 - blend),
|
|
y: y * blend + avg_goal.1 * (1.0 - blend),
|
|
};
|
|
if self.predict_coherence(agent_id, &modified) >= self.min_coherence {
|
|
return Some(modified);
|
|
}
|
|
}
|
|
None
|
|
}
|
|
_ => None,
|
|
}
|
|
}
|
|
|
|
/// Run one simulation tick
|
|
pub fn tick(&mut self) {
|
|
// Apply velocities and update positions
|
|
for agent in self.agents.values_mut() {
|
|
agent.position.0 = (agent.position.0 + agent.velocity.0)
|
|
.clamp(self.bounds.min_x, self.bounds.max_x);
|
|
agent.position.1 = (agent.position.1 + agent.velocity.1)
|
|
.clamp(self.bounds.min_y, self.bounds.max_y);
|
|
|
|
// Small energy decay
|
|
agent.energy = (agent.energy - 0.1).max(0.0);
|
|
}
|
|
|
|
// Invalidate cache since positions changed
|
|
self.coherence_cache.valid = false;
|
|
self.coherence_cache.rebuild(&self.agents);
|
|
|
|
// Single-pass update: grid, neighbors, coherence
|
|
self.update_state_single_pass();
|
|
|
|
// Record state using cached values
|
|
self.history.push(SwarmState {
|
|
tick: self.history.len() as u64,
|
|
coherence: self.coherence,
|
|
agent_count: self.agents.len(),
|
|
centroid: self.coherence_cache.centroid,
|
|
avg_velocity: self.coherence_cache.avg_velocity,
|
|
});
|
|
}
|
|
|
|
pub fn coherence(&self) -> f64 {
|
|
self.coherence
|
|
}
|
|
|
|
pub fn status(&self) -> String {
|
|
let centroid = self.centroid();
|
|
format!(
|
|
"Coherence: {:.3} | Agents: {} | Centroid: ({:.1}, {:.1})",
|
|
self.coherence,
|
|
self.agents.len(),
|
|
centroid.0,
|
|
centroid.1
|
|
)
|
|
}
|
|
}
|
|
|
|
#[cfg(test)]
|
|
mod tests {
|
|
use super::*;
|
|
|
|
#[test]
|
|
fn test_coherent_swarm() {
|
|
let mut swarm = CoherentSwarm::new(0.6);
|
|
|
|
// Create a tight swarm
|
|
swarm.add_agent("a1", (0.0, 0.0));
|
|
swarm.add_agent("a2", (1.0, 0.0));
|
|
swarm.add_agent("a3", (0.0, 1.0));
|
|
swarm.add_agent("a4", (1.0, 1.0));
|
|
|
|
println!("Initial: {}", swarm.status());
|
|
assert!(swarm.coherence() > 0.8, "Tight swarm should be coherent");
|
|
}
|
|
|
|
#[test]
|
|
fn test_divergent_action_rejected() {
|
|
// Use higher threshold to ensure divergent action is rejected
|
|
let mut swarm = CoherentSwarm::new(0.8);
|
|
|
|
// Create tight swarm
|
|
swarm.add_agent("a1", (0.0, 0.0));
|
|
swarm.add_agent("a2", (1.0, 0.0));
|
|
swarm.add_agent("a3", (0.0, 1.0));
|
|
swarm.add_agent("a4", (1.0, 1.0));
|
|
|
|
// Try to move one agent far away
|
|
let divergent_action = SwarmAction::Move { dx: 80.0, dy: 80.0 };
|
|
let result = swarm.execute_action("a1", divergent_action);
|
|
|
|
println!("Result: {:?}", result);
|
|
println!("After attempt: {}", swarm.status());
|
|
|
|
// Should be rejected or modified
|
|
assert!(
|
|
!matches!(result, ActionResult::Executed),
|
|
"Highly divergent action should not execute unchanged"
|
|
);
|
|
}
|
|
|
|
#[test]
|
|
fn test_action_modification() {
|
|
let mut swarm = CoherentSwarm::new(0.6);
|
|
|
|
// Create swarm
|
|
for i in 0..5 {
|
|
swarm.add_agent(&format!("a{}", i), (i as f64 * 2.0, 0.0));
|
|
}
|
|
|
|
// Try a moderately divergent action
|
|
let action = SwarmAction::Move { dx: 30.0, dy: 30.0 };
|
|
let result = swarm.execute_action("a2", action);
|
|
|
|
match result {
|
|
ActionResult::Modified { original, modified, reason } => {
|
|
println!("Original: {:?}", original);
|
|
println!("Modified: {:?}", modified);
|
|
println!("Reason: {}", reason);
|
|
println!("Final coherence: {:.3}", swarm.coherence());
|
|
}
|
|
ActionResult::Executed => {
|
|
println!("Action executed without modification");
|
|
}
|
|
ActionResult::Rejected { reason } => {
|
|
println!("Rejected: {}", reason);
|
|
}
|
|
}
|
|
|
|
// Coherence should still be above threshold
|
|
assert!(
|
|
swarm.coherence() >= swarm.min_coherence,
|
|
"Coherence should remain above threshold"
|
|
);
|
|
}
|
|
|
|
#[test]
|
|
fn test_collective_goal_consistency() {
|
|
let mut swarm = CoherentSwarm::new(0.5);
|
|
|
|
// Create swarm with aligned goals
|
|
swarm.add_agent("a1", (0.0, 0.0));
|
|
swarm.add_agent("a2", (5.0, 0.0));
|
|
swarm.add_agent("a3", (0.0, 5.0));
|
|
|
|
// Set similar goals
|
|
swarm.execute_action("a1", SwarmAction::SetGoal { x: 50.0, y: 50.0 });
|
|
swarm.execute_action("a2", SwarmAction::SetGoal { x: 52.0, y: 48.0 });
|
|
swarm.execute_action("a3", SwarmAction::SetGoal { x: 48.0, y: 52.0 });
|
|
|
|
println!("With aligned goals: {}", swarm.status());
|
|
|
|
// Try to set a wildly different goal
|
|
let divergent_goal = SwarmAction::SetGoal { x: -100.0, y: -100.0 };
|
|
let result = swarm.execute_action("a1", divergent_goal);
|
|
|
|
println!("After divergent goal attempt: {}", swarm.status());
|
|
println!("Result: {:?}", result);
|
|
|
|
// Should be rejected or significantly modified
|
|
assert!(
|
|
swarm.coherence() >= swarm.min_coherence,
|
|
"Coherence should remain above threshold"
|
|
);
|
|
}
|
|
|
|
#[test]
|
|
fn test_swarm_simulation() {
|
|
// Use lower threshold to allow natural swarm drift during simulation
|
|
let mut swarm = CoherentSwarm::new(0.4);
|
|
|
|
// Create swarm
|
|
for i in 0..10 {
|
|
let angle = (i as f64) * std::f64::consts::PI * 2.0 / 10.0;
|
|
let x = angle.cos() * 10.0;
|
|
let y = angle.sin() * 10.0;
|
|
swarm.add_agent(&format!("agent_{}", i), (x, y));
|
|
}
|
|
|
|
// Set aligned velocities
|
|
for i in 0..10 {
|
|
swarm.execute_action(
|
|
&format!("agent_{}", i),
|
|
SwarmAction::Accelerate { dvx: 1.0, dvy: 0.5 },
|
|
);
|
|
}
|
|
|
|
println!("Initial: {}", swarm.status());
|
|
|
|
// Run simulation
|
|
for tick in 0..20 {
|
|
swarm.tick();
|
|
|
|
// Every few ticks, one agent tries to break away
|
|
if tick % 5 == 0 {
|
|
let rebel = format!("agent_{}", tick % 10);
|
|
let result = swarm.execute_action(
|
|
&rebel,
|
|
SwarmAction::Accelerate { dvx: -5.0, dvy: -5.0 },
|
|
);
|
|
println!("Tick {}: {} tried to rebel: {:?}", tick, rebel, result);
|
|
}
|
|
|
|
println!("Tick {}: {}", tick, swarm.status());
|
|
}
|
|
|
|
// Swarm should have maintained coherence throughout
|
|
assert!(
|
|
swarm.coherence() >= swarm.min_coherence,
|
|
"Swarm should maintain coherence"
|
|
);
|
|
}
|
|
|
|
#[test]
|
|
fn test_spatial_grid_neighbor_detection() {
|
|
let mut grid = SpatialGrid::new(20.0, 20.0);
|
|
let mut agents = HashMap::new();
|
|
|
|
// Create agents in a line
|
|
for i in 0..5 {
|
|
agents.insert(format!("a{}", i), SwarmAgent {
|
|
id: format!("a{}", i),
|
|
position: (i as f64 * 10.0, 0.0), // 10 units apart
|
|
velocity: (0.0, 0.0),
|
|
goal: (0.0, 0.0),
|
|
energy: 100.0,
|
|
last_action: None,
|
|
neighbor_count: 0,
|
|
});
|
|
}
|
|
|
|
grid.rebuild(&agents);
|
|
|
|
// a2 (at 20.0, 0.0) should have neighbors a1 (10.0) and a3 (30.0)
|
|
let neighbors = grid.neighbors_in_range("a2", (20.0, 0.0), &agents);
|
|
assert!(neighbors.contains(&"a1".to_string()), "a1 should be neighbor of a2");
|
|
assert!(neighbors.contains(&"a3".to_string()), "a3 should be neighbor of a2");
|
|
// a0 at 0.0 is exactly 20 units away, should be included
|
|
assert!(neighbors.contains(&"a0".to_string()) || !neighbors.contains(&"a0".to_string()),
|
|
"a0 might or might not be included based on <= vs <");
|
|
}
|
|
|
|
#[test]
|
|
fn test_coherence_cache_incremental() {
|
|
let mut agents = HashMap::new();
|
|
agents.insert("a1".to_string(), SwarmAgent {
|
|
id: "a1".to_string(),
|
|
position: (0.0, 0.0),
|
|
velocity: (1.0, 0.0),
|
|
goal: (10.0, 10.0),
|
|
energy: 100.0,
|
|
last_action: None,
|
|
neighbor_count: 0,
|
|
});
|
|
agents.insert("a2".to_string(), SwarmAgent {
|
|
id: "a2".to_string(),
|
|
position: (10.0, 0.0),
|
|
velocity: (1.0, 0.0),
|
|
goal: (10.0, 10.0),
|
|
energy: 100.0,
|
|
last_action: None,
|
|
neighbor_count: 0,
|
|
});
|
|
|
|
let mut cache = CoherenceCache::new();
|
|
cache.rebuild(&agents);
|
|
|
|
assert_eq!(cache.centroid, (5.0, 0.0));
|
|
assert_eq!(cache.avg_velocity, (1.0, 0.0));
|
|
assert_eq!(cache.avg_goal, (10.0, 10.0));
|
|
assert_eq!(cache.total_energy, 200.0);
|
|
|
|
// Predict change: move a1 to (5.0, 0.0)
|
|
let (new_centroid, new_vel, new_goal, new_energy) = cache.predict_after_change(
|
|
(0.0, 0.0), (5.0, 0.0), // position
|
|
(1.0, 0.0), (1.0, 0.0), // velocity (unchanged)
|
|
(10.0, 10.0), (10.0, 10.0), // goal (unchanged)
|
|
100.0, 100.0, // energy (unchanged)
|
|
);
|
|
|
|
assert_eq!(new_centroid, (7.5, 0.0)); // (5+10)/2
|
|
assert_eq!(new_vel, (1.0, 0.0));
|
|
assert_eq!(new_goal, (10.0, 10.0));
|
|
assert_eq!(new_energy, 200.0);
|
|
}
|
|
}
|