What is optimal in optimal inference?

Area of Science:

• Cognitive Science
• Machine Learning
• Statistical Inference

Background:

• Inferring hidden structure from noisy data is a fundamental challenge for intelligent agents.
• Bayesian statistical learning provides a framework for identifying optimal models of data-generating processes.

Purpose of the Study:

• To review how intelligent agents address model-selection problems.
• To explore the relationship between agent solutions and Bayesian principles.
• To propose a framework for assessing inference optimality under constraints.

Main Methods:

• Review of recent literature on intelligent agents and Bayesian inference.
• Focus on information and resource constraints affecting inference.
• Development of a general framework using benefit-accuracy and accuracy-cost curves.

Main Results:

• Intelligent agents and machines face similar model-selection challenges.
• Bayesian principles offer insights into how agents infer latent properties and predict future states.
• Constraints significantly affect inference processes.

Conclusions:

• A novel framework using benefit-accuracy and accuracy-cost curves can assess inference optimality.
• Understanding these constraints is crucial for developing more effective intelligent agents.
• The study bridges cognitive science and machine learning through the lens of Bayesian inference.