The Free Will Equation: Quantum Field Analogies for AGI

TL;DR

This paper introduces the Free Will Equation, a quantum field-inspired framework for AGI that enables adaptive, stochastic decision-making, leading to improved exploration, diversity, and performance in dynamic environments.

Contribution

It proposes a novel quantum field analogy for AGI decision processes, integrating intrinsic motivation to enhance adaptability and creativity.

Findings

Agents with the framework achieve higher rewards.

Increased policy diversity observed in experiments.

Enhanced adaptability in non-stationary environments.

Abstract

Artificial General Intelligence (AGI) research traditionally focuses on algorithms that optimize for specific goals under deterministic rules. Yet, human-like intelligence exhibits adaptive spontaneity - an ability to make unexpected choices or free decisions not strictly dictated by past data or immediate reward. This trait, often dubbed "free will" in a loose sense, might be crucial for creativity, robust adaptation, and avoiding ruts in problem-solving. This paper proposes a theoretical framework, called the Free Will Equation, that draws analogies from quantum field theory to endow AGI agents with a form of adaptive, controlled stochasticity in their decision-making process. The core idea is to treat an AI agent's cognitive state as a superposition of potential actions or thoughts, which collapses probabilistically into a concrete action when a decision is made - much like a quantum wavefunction collapsing upon measurement. By incorporating mechanisms analogous to quantum fields, along with intrinsic motivation terms, we aim to improve an agent's ability to explore novel strategies and adapt to unforeseen changes. Experiments in a non-stationary multi-armed bandit environment demonstrate that agents using this framework achieve higher rewards and policy diversity compared to baseline methods.