How the architecture of utility based agent is different from Model based agent in ai

The architecture of a utility-based agent differs from that of a model-based agent in AI. Let's break down the differences step by step:

1. Utility-based agent:

   • A utility-based agent focuses on maximizing its overall utility or goal achievement.
   • It uses a utility function to evaluate the desirability of different actions or outcomes.
   • The agent selects the action that maximizes its expected utility based on the current state and available information.
   • The architecture of a utility-based agent typically includes a decision-making component that computes the expected utility of different actions and a utility function that assigns values to different outcomes.

2. Model-based agent:

   • A model-based agent focuses on building and maintaining an internal model of the world.
   • It uses this model to make predictions about the future and plan its actions accordingly.
   • The agent updates its model based on new observations and uses it to simulate different scenarios and evaluate the consequences of different actions.
   • The architecture of a model-based agent typically includes a model of the world, a planning component that uses the model to generate action sequences, and a learning component that updates the model based on new observations.

In summary, the key difference between the architecture of a utility-based agent and a model-based agent lies in their focus and approach. While a utility-based agent aims to maximize overall utility, a model-based agent focuses on building and using an internal model of the world to make predictions and plan its actions.