Agent Decision Engine#

Autonomous decision engine for AI agents with multi-objective optimization, risk assessment, decision trees, and reinforcement learning capabilities.

Features#

• Multi-Objective Optimization: Pareto optimization with configurable weights and constraints
• Risk Assessment: Probability evaluation, impact analysis, and risk matrices
• Decision Trees: Build, evaluate, prune, and visualize decision paths
• Reinforcement Learning: Q-Learning with customizable reward functions

Usage#

javascript

import { DecisionEngine } from './src/index.js';

const engine = new DecisionEngine();

// Multi-objective optimization
const result = engine.optimize([
  { name: 'cost', value: 100, weight: 0.4, minimize: true },
  { name: 'quality', value: 85, weight: 0.6, minimize: false }
]);

// Risk assessment
const risk = engine.assessRisk({
  probability: 0.3,
  impact: 0.8,
  mitigation: ['backup plan', 'monitoring']
});

// Decision tree
const tree = engine.buildDecisionTree({
  options: ['A', 'B', 'C'],
  outcomes: [0.7, 0.5, 0.9]
});

// Q-Learning
const action = engine.qLearn({
  state: [1, 0, 1],
  actions: ['move', 'stay', 'attack'],
  reward: 10
});

API#

DecisionEngine#

Main class combining all decision-making capabilities.

optimize(objectives, constraints)

Multi-objective optimization with Pareto front.

assessRisk(riskConfig)

Evaluate and score risks.

buildDecisionTree(config)

Build and evaluate decision trees.

qLearn(config)

Q-Learning for sequential decision making.

License#

MIT