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Chapter 1 - Introduction to AI
Part 2
Define Agent in AI -:
In Artificial Intelligence (AI), an agent refers to an entity that perceives its environment through sensors, processes that information, and takes actions to achieve specific goals through actuators. AI agents operate autonomously, meaning they can make decisions and act without human intervention, based on the data they receive and the goals they are designed to pursue.
Key Characteristics of an AI Agent:
- Autonomy: The agent can operate independently in its environment without constant human guidance.
- Perception: It senses the environment using various input mechanisms (e.g., cameras, microphones, sensors).
- Action: Based on its perception, it takes appropriate actions to influence the environment (e.g., moving, changing states).
- Learning: Many agents can improve their performance over time using machine learning algorithms.
- Goal-Oriented Behavior: Agents are designed to achieve specific objectives.
Types of AI Agents:
Simple Reflex Agents: These agents act only based on the current perception, without considering the history of states. They use condition-action rules to decide on actions.
Model-Based Reflex Agents: These agents maintain an internal state of the world and make decisions based on both current perceptions and historical data.
Goal-Based Agents: They not only react to the environment but also have specific goals that they try to achieve. These agents choose actions that bring them closer to their goals.
Utility-Based Agents: In addition to goals, these agents have a utility function that measures the “happiness” or “satisfaction” of achieving certain outcomes, allowing them to choose actions that maximize their utility.
Learning Agents: These agents improve their performance over time by learning from the environment and past experiences.
Example of AI Agents:
- Autonomous Vehicles: The car perceives the environment (e.g., other cars, traffic signals) and makes decisions (e.g., braking, turning) to navigate safely.
- Robots: Industrial robots in manufacturing can sense their surroundings and act to assemble parts autonomously.
- Virtual Assistants: AI systems like Siri or Google Assistant perceive user commands and respond with actions like answering questions or setting reminders.
1. Simple Reflex Agents
These agents act solely based on the current percept (what they sense at the moment) and do not consider the history of previous percepts. They respond to their environment using condition-action rules, often described as if-then rules.
Characteristics:
- No memory of past actions or percepts.
- Act only based on the current state of the environment.
- Efficient in predictable and simple environments.
Example:
A thermostat that controls the heating in a house is a simple reflex agent. It senses the current temperature and acts to turn the heater on or off based on preset conditions (e.g., if the temperature falls below 20°C, turn on the heater).
Limitation:
Simple reflex agents struggle in complex environments where history or additional context is needed to make the right decision.
2. Model-Based Reflex Agents
Unlike simple reflex agents, model-based agents maintain an internal model of the world. They use this model to keep track of parts of the environment that they can’t directly observe at any given moment. This internal state is updated based on the agent’s perception and past actions.
Characteristics:
- Maintains a memory (internal state) to model the environment.
- Can handle more complex environments than simple reflex agents.
- Uses its model to predict the effects of its actions.
Example:
A self-driving car that tracks not only the current positions of nearby cars but also predicts their future movements based on past trajectories.
Limitation:
The complexity of maintaining an accurate model can grow quickly in dynamic or uncertain environments.
3. Goal-Based Agents
Goal-based agents work by having specific goals they aim to achieve. Instead of just reacting to stimuli, they choose actions that bring them closer to achieving their goals. To do this, they often need to search or plan to determine which actions will help them reach their objective.
Characteristics:
- Capable of long-term decision-making by considering future consequences.
- The agent selects actions that are expected to help it achieve its goals.
- Requires more computation than reflex agents because it may need to plan ahead.
Example:
A robot tasked with delivering packages might have a goal to deliver the package to a specific location. It will plan a route and decide the best path to take based on the goal of reaching that location.
Limitation:
Finding the right sequence of actions can be computationally expensive, especially in large, dynamic environments.
4. Utility-Based Agents
Utility-based agents not only aim to achieve specific goals but also maximize a utility function, which is a numerical value representing the “happiness” or “satisfaction” of the agent. Utility helps the agent choose between different actions when there are multiple ways to achieve a goal or when the goal itself is not strictly defined. These agents consider both the likelihood of achieving a goal and the value of that goal.
Characteristics:
- Balances between different goals and tries to maximize a utility function.
- Can make trade-offs between competing objectives or uncertain outcomes.
- More flexible than simple goal-based agents because it considers the “best” action rather than just any action that achieves a goal.
Example:
An investment AI might have the goal of making profitable investments, but it uses a utility function to balance risk against potential reward, choosing investments that maximize expected profit.
Limitation:
Creating an appropriate utility function that correctly captures all the trade-offs in real-world scenarios can be difficult.
5. Learning Agents
Learning agents improve their performance over time by learning from their experiences and the environment. They have the ability to modify their behavior based on feedback from past actions. A learning agent is typically composed of the following components:
- Learning Element: Responsible for making improvements by learning from experience.
- Performance Element: Decides what actions to take based on the agent’s knowledge.
- Critic: Provides feedback on the agent’s actions by comparing actual outcomes with desired outcomes.
- Problem Generator: Suggests new actions to help the agent explore the environment and improve.
Characteristics:
- Capable of adapting and improving over time.
- Suitable for environments where the agent’s knowledge of the environment is incomplete or changing.
- Can generalize from past experiences to make better decisions in the future.
Example:
A chess-playing AI that improves its strategy as it plays more games, learning from its mistakes and adjusting its moves to increase its chances of winning.
Limitation:
Learning may take time and computational resources, and the agent might perform poorly during the learning phase.
end of part 2 -------->
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