types of AI agents | Part-2/2 | learning, goal & utility based | Lec-7| Bhanu Priya

Introduction

This tutorial covers the different types of artificial intelligence (AI) agents, specifically focusing on learning-based, goal-based, and utility-based agents. Understanding these categories is essential for anyone interested in AI development, as each type serves distinct purposes and utilizes different methodologies.

Step 1: Understanding Learning-Based Agents

Learning-based agents improve their performance over time by acquiring knowledge from their experiences. These agents adapt to changes in their environment and enhance their decision-making capabilities.

Key Characteristics:

• Self-Improvement: They learn from past actions and outcomes.
• Experience-Based: The more they interact with their environment, the better they become.

Practical Advice:

• Implement reinforcement learning techniques to develop a learning-based agent.
• Use algorithms such as Q-learning or deep learning to facilitate the learning process.

Step 2: Exploring Goal-Based Agents

Goal-based agents operate with specific objectives in mind. They evaluate their actions based on how well those actions help them achieve their goals.

Key Characteristics:

• Goal-Oriented: Focused on achieving specific objectives.
• Decision-Making: They can plan and make decisions that align with their goals.

Practical Advice:

• Define clear goals for your agent to ensure focused behavior.
• Consider using search algorithms to help the agent determine the best path to achieve its goals.

Step 3: Investigating Utility-Based Agents

Utility-based agents make decisions based on a utility function, which quantifies the desirability of different states or actions. These agents aim to maximize their overall satisfaction or utility.

Key Characteristics:

• Preference-Based: They evaluate outcomes based on their preferences.
• Optimization: They seek the most optimal solution among various choices.

Practical Advice:

• Develop a utility function that accurately reflects the agent's preferences.
• Use decision theory principles to guide the agent’s choices.

Conclusion

In this tutorial, we explored three main types of AI agents: learning-based, goal-based, and utility-based. Each type has its unique characteristics and applications. Understanding these distinctions can help you choose the right type of agent for your AI projects.

Next steps could include experimenting with simple implementations of each agent type using programming languages like Python, and exploring libraries such as TensorFlow or OpenAI Gym for practical applications.