The main goals of Artificial Intelligence are to replicate human intelligence, solve complex knowledge-based problems, and connect perception with action. AI also aims to develop systems that can learn, reason, plan, and perform tasks like humans.
Example: Self-driving cars that can learn driving patterns and make decisions on roads.
AI tries to mimic human brain functions such as thinking, learning, and decision-making.
It is designed to reduce human effort and improve efficiency in real-world tasks.
Real-time Analogy: Like a smart assistant (Google Assistant) that understands your voice, thinks, and gives correct responses.
Simple reflex agents act only on current input using condition-action rules, whereas model-based agents use memory and an internal model to make better decisions based on past and present data.
Example:
- Simple → Light turns ON when switch is pressed
- Model-based → Smart thermostat adjusts temperature based on past usage
Model-based agents are more intelligent because they understand the environment, while simple agents react blindly without memory.
Real-time Analogy: Simple → Automatic door opens | Model-based → Google Maps uses past + current traffic
Artificial Intelligence is a branch of computer science that creates systems capable of performing tasks like learning, reasoning, problem solving, and decision making similar to humans.
Example: Chatbots used in customer service
AI is not just robots; it includes software systems like recommendation engines, chatbots, etc.
Real-time Analogy: ChatGPT answering like a human tutor
Steps include problem definition, data collection, model selection, solution generation, evaluation, and refinement.
Example: Predicting student marks using past data
This structured approach helps AI find optimal solutions efficiently.
Real-time Analogy: Understand → Solve → Check → Improve
Two types: logical representation and semantic networks.
Example:
- Logical → “All birds can fly”
- Semantic → Relationship diagrams
Different techniques are used depending on complexity.
Analogy: Logical → “Humans are mortal” | Semantic → Family tree
They use a knowledge base (facts + rules) and reasoning mechanism to make decisions.
Example: Medical expert system
They continuously update knowledge for better decisions.
Analogy: Doctor diagnosing disease
- Factual → Water boils at 100°C
- Procedural → Steps to solve math
- Heuristic → Shortcuts
Heuristic knowledge is faster but not always perfect.
Analogy: Facts, Steps, Tricks
Includes objects, predicates, variables, functions, and quantifiers (∀, ∃).
Example: ∀x Human(x) → Mortal(x)
FOL is more powerful than propositional logic.
Used in theorem proving, NLP, expert systems.
Example: Chatbot matching queries
Helps match patterns and variables.
A plan is a sequence of actions to achieve a goal.
Types: Total order, Partial order
Planning helps decide next steps.
Requires matching predicates, same arguments, and consistent substitutions.
Incorrect matching causes errors.
Used in robotics and scheduling.
Used in automation systems.
Actions are flexible and not strictly ordered.
Allows parallel execution.
Handles constraints like time, money, resources.
Efficient allocation is key.
Goal → Action → Constraints → Conflict resolution → Refinement
Avoids unnecessary restrictions.
Helps organize tasks, reduce wastage, improve efficiency.
Acts as blueprint.
Used to handle uncertainty and predict outcomes.
Example: Rain probability 70%
Used when exact answers are not possible.
Updates probability using evidence (Prior + Posterior).
Improves accuracy.
Calculates updated probability using evidence.
Widely used in ML.
Variables are independent given a third variable.
Reduces complexity.
0 Comments