G.O.D Framework

Introduction

The ai_free_will.py module is an experimental component in the G.O.D Framework designed to simulate autonomous decision-making mechanisms in AI systems. Inspired by the concept of "free will," this module enables AI agents to make decisions based on multiple weighted factors, environmental contexts, and ethical considerations.

By implementing decision strategies, this module empowers AI systems to operate with greater independence while adhering to organizational and ethical constraints.

Purpose

• Provide autonomous decision-making capabilities to AI agents under specified constraints.
• Simulate weighted decision paths based on core and external factors.
• Ensure AI decisions align with ethical boundaries and organizational goals.
• Serve as a foundational model for emergent behavior algorithms.
• Create flexible workflows for dynamically prioritizing multiple objectives.

Key Features

• Decision Weighting: AI agents evaluate decisions based on assigned weights and context-driven importance levels.
• Goal Prioritization: Multiple objectives are mapped and prioritized dynamically to resolve conflicts.
• Ethical Constraints: Implements ethical and legality checks before executing a decision.
• Adaptability: Adapts decision-making behavior in response to environmental changes or new inputs.
• Logging and Explainability: Comprehensive decision logs and reasoning chains for retrospection and debugging.

Logic and Implementation

The central logic of ai_free_will.py revolves around a DecisionEngine that scores and processes possible decisions, then selects the optimal option based on context and constraints. Below is an example interpretation of how this module works:

            import random

            class DecisionEngine:
                """
                Decision-making engine that evaluates and prioritizes alternatives based on weights and constraints.
                """

                def __init__(self, context_data, ethical_constraints):
                    """
                    Initialize the engine with context and constraints.
                    :param context_data: Data about current context/environment.
                    :param ethical_constraints: Rules that must not be violated during decision-making.
                    """
                    self.context_data = context_data
                    self.constraints = ethical_constraints

                def evaluate_decisions(self, decision_options):
                    """
                    Score and rank possible decisions.
                    :param decision_options: List of decisions with associated weights and metadata.
                    Example: [{"name": "Option A", "weight": 0.8}, {"name": "Option B", "weight": 0.4}]
                    :return: Sorted list of decisions with their scores.
                    """
                    scored_options = []
                    for option in decision_options:
                        score = self._calculate_score(option)
                        if self._check_constraints(option):
                            scored_options.append({**option, "score": score})
                        else:
                            print(f"Option '{option['name']}' violates constraints!")

                    return sorted(scored_options, key=lambda x: x["score"], reverse=True)

                def _check_constraints(self, option):
                    """
                    Validate the decision against constraints (e.g., ethical rules).
                    :param option: Decision metadata.
                    """
                    # Simplified constraint validation
                    for rule in self.constraints:
                        if not rule(option):
                            return False
                    return True

                def _calculate_score(self, option):
                    """
                    Compute a score dynamically based on weight and random environmental factors.
                    :param option: Decision metadata.
                    """
                    random_factor = random.uniform(0.8, 1.2)
                    return option["weight"] * random_factor

                def execute_decision(self, decision):
                    """
                    Execute the chosen decision and log outcome.
                    """
                    print(f"Executing decision: {decision['name']} with score {decision['score']}")

            # Example Usage
            if __name__ == "__main__":
                # Setup context and constraints
                context = {"task_load": 3, "current_energy": 0.9}
                constraints = [
                    lambda d: d["weight"] > 0.5  # Example constraint: Minimum weight threshold
                ]

                # Initialize decision engine
                engine = DecisionEngine(context, constraints)

                # Possible decisions
                options = [
                    {"name": "Allocate Resources", "weight": 0.8},
                    {"name": "Pause Task", "weight": 0.9},
                    {"name": "Abort Mission", "weight": 0.4}
                ]

                # Evaluate and pick the best
                ranked_decisions = engine.evaluate_decisions(options)
                best_decision = ranked_decisions[0]
                engine.execute_decision(best_decision)
            

Dependencies

This module relies on the following libraries:

• random: For introducing stochasticity in the decision evaluation process.
• Custom Constraint Functions: User-defined functions for tailored ethical validations.
• No external dependencies (lightweight module).

Usage

1. Define the context/environment data relevant to decision-making.
2. Define constraints (rules/tests) as functions to enforce ethical/legal compliance.
3. Create a list of alternative decisions with metadata (e.g., weights, descriptions).
4. Pass this information to the DecisionEngine for evaluation and execution.

            # Run AI Free Will's Decision Engine
            context = {"current_energy": 0.7}
            constraints = [lambda d: "weight" in d and d["weight"] > 0.5]

            decisions = [
                {"name": "Option A", "weight": 0.6},
                {"name": "Option B", "weight": 0.4}
            ]

            engine = DecisionEngine(context, constraints)
            best_decision = engine.evaluate_decisions(decisions)[0]
            engine.execute_decision(best_decision)
            

System Integration

• AI Agent Integration: Embeds decision-making capabilities into chatbot agents or robotics frameworks.
• Task Automation: Used in workflow managers to autonomously schedule and execute tasks.
• Distributed Systems: Supports scalable decision-making in distributed or cloud-based systems.

Future Enhancements

• Introduce reinforcement learning techniques for adaptive decision strategies.
• Expand ethical constraints with context-aware policies and law-based compliance engines.
• Develop plug-and-play interfaces for real-time environmental factor ingestion.