![AI](/wp-content/uploads/2022/11/AI.png)
AI is branch of computer science by which we can create intelligent machine which can behave like a human, think like humans and able to make decisions.
Examples
- Chatbots use AI to understand customer problems faster and provide more efficient answers
- Intelligent assistants use AI to parse critical information from large free-text datasets to improve scheduling
- Recommendation engines can provide automated recommendations for TV shows based on users’ viewing habits
Goals of AI
- Replicate Human Intelligence
- Solve Knowledge-Intensive tasks
- An Intelligent Connection of perception and action
- Building a machine which can perform tasks that requires human intelligence such as
- Providing a theorem
- Playing Chess
- Plan some surgical operation
- Driving a car in traffic
What is Intelligent Components in AI?
AI has focused on the following components of intelligence:
learning, reasoning, problem solving, perception, and linguistic intelligence
Agents in AI
What is an Agent?
An agent can be anything that perceive environment through sensors & act upon that environment through actuators.
Three types of agents:
- Human Agent -> Eyes, Ears & other organs which work for sensors
- Robotic Agent -> Cameras, Infrared Range Finder
- Software Agent -> Key Strokes
Types of AI Agents:
Agents can be grouped into five classes based on their degree of perceived intelligence & capacity.
- Simple Reflex Agent
- Model-based reflex agent
- Goal based agents
- Utility based agents
- Learning Agents
1. Simple Reflex Agent
- This agent works only on the basis of current perception
- It does not bother about the previous state in which the system was.
- This type of agent is based upon the condition-action rule. If condition is true then action is taken else not.
Problem Faced in Simple Reflex Agent
- Very Limited Intelligence
- No knowledge about the non-perceptional parts of the state
- operating in a partially observable environment, infinite loops are unavailable
2. Model based Reflex Agent
- It works by finding a rule whose condition matches the current situation
- It can handle partially observable environment
- Updating the state requires information about
- how world evolves independently from the agent
- how the agent actions affect the world
3. Goal Based Agents
- It focus only on reaching the goal set and hence the decision took by the agent is based on how far it is currently from their goal of desired state.
- Every action is intended to minimize their distance from the goal
- Decision Making skill by choosing the right from the various options available
- more flexible
4. Utility based Agents
- Similar to goal based agent
- it act based not only goals but also the best way to achieve the goal
5. Learning Agent
It can learn from its past experience or it has a learning capabilities
4 Conceptual Components
- Learning element
- Critic
- Performance Element
- Problem Generates