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
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