agentjs-core/dist/ml/models/FlockingMLModel.d.ts

A comprehensive agent-based modeling framework with built-in p5.js visualization

import { MLBehaviorModel, MLAgentState, AgentAction } from '../interfaces';
/**
 * Generic flocking behavior ML model
 * Works for birds, fish, crowds, or any swarm behavior
 */
export declare class FlockingMLModel implements MLBehaviorModel {
    private model?;
    private inputFeatures;
    private outputActions;
    private isLoaded;
    private separationWeight;
    private alignmentWeight;
    private cohesionWeight;
    private maxSpeed;
    constructor();
    predict(state: MLAgentState): Promise<AgentAction>;
    /**
     * ML-based prediction
     */
    private predictWithML;
    /**
     * Rule-based flocking prediction (fallback and training data generation)
     */
    private predictWithRules;
    /**
     * Calculate separation force (avoid crowding neighbors)
     */
    private calculateSeparation;
    /**
     * Calculate alignment force (steer towards average heading of neighbors)
     */
    private calculateAlignment;
    /**
     * Calculate cohesion force (steer towards center of neighbors)
     */
    private calculateCohesion;
    /**
     * Calculate boundary avoidance force
     */
    private calculateBoundaryAvoidance;
    /**
     * Encode agent state for ML model input
     */
    private encodeFlockingState;
    /**
     * Decode ML output to agent action
     */
    private decodeFlockingAction;
    load(modelPath: string): Promise<void>;
    getRequiredInputs(): string[];
    getOutputActions(): string[];
    dispose(): void;
    /**
     * Configure flocking behavior parameters
     */
    configure(options: {
        separationWeight?: number;
        alignmentWeight?: number;
        cohesionWeight?: number;
        maxSpeed?: number;
    }): void;
    /**
     * Generate training data for this model
     */
    generateTrainingData(scenarios: Array<{
        agentCount: number;
        bounds: {
            width: number;
            height: number;
        };
    }>, stepsPerScenario?: number): Array<{
        input: number[];
        output: number[];
    }>;
    /**
     * Generate mock agent state for training data
     */
    private generateMockState;
}
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