Beliefs implemented in a ML model

Beliefs can be implemented in a model.
I realized that actually beliefs are a fundamental component of many cognitive services.

First, what is a belief?
Can be defined as a mental attitude or conviction that something is true or real and more than this, everybody must accept this as true and real.
This is happening in the absence of absolute proof or certainty.
Belief is a subjective feeling or attitude that a person holds about the world or themselves. Beliefs can be influenced by various factors, such as personal experience, cultural background, education, and social environment.

In Machine Learning, beliefs can be represented in different ways, depending on the specific model and its goals.

• Bayesian network - beliefs are represented as probabilities assigned to different variables.
• Reinforcement learning model - beliefs can be represented as a value function that estimates the expected future reward of different actions (feedback from environment).
• Cognitive architecture - beliefs can be represented as mental models that capture a person's ability to understand external environment.

Most common creating a model that will predict beliefs is done using Bayes' rule or reinforcement learning techniques.

Beliefs are a key component of many cognitive and AI models
Implementing beliefs is essential for building models that can properly react to complex environments.