📘 PAPER 3: ARTIFICIAL INTELLIGENCE & ROBOTICS UNIT 1 – INTRODUCTION TO AI & SEARCH TECHNIQUES (university of allahabad)

 

🔴 UNIT 1 – INTRODUCTION TO AI & SEARCH TECHNIQUES

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1️⃣ Introduction to Artificial Intelligence (AI)

✅ Definition of AI

Artificial Intelligence is a branch of computer science that deals with creating intelligent machines capable of performing tasks that normally require human intelligence.

Examples:

• Speech recognition

• Face detection

• Self-driving cars

• Chatbots

• Robotics

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2️⃣ Scope of Artificial Intelligence

AI is widely used in the following fields:

🔹 1. Games

• Chess

• Ludo

• Video games

• AlphaGo

🔹 2. Theorem Proving

• Logical reasoning

• Mathematical proof verification

🔹 3. Natural Language Processing (NLP)

• Language translation

• Chatbots

• Speech recognition

🔹 4. Vision Systems

• Face recognition

• Object detection

• Medical image analysis

🔹 5. Robotics

• Industrial robots

• Medical robots

• Space robots

🔹 6. Expert Systems

• Medical diagnosis

• Fault detection

• Decision-making systems

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3️⃣ AI Techniques

AI techniques are methods used to solve complex problems.

Important AI Techniques:

• Search techniques

• Knowledge representation

• Heuristic methods

• Machine learning

• Reasoning techniques

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4️⃣ Intelligent Agents

✅ Definition

An Intelligent Agent is an entity that:

• Perceives environment using sensors

• Acts using actuators

• Makes decisions intelligently

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Components of Intelligent Agent:

Component	Description
Sensors	Collect information
Actuators	Perform actions
Environment	Where agent operates
Agent function	Maps perception to action

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Types of Agents

1. Simple reflex agent

2. Model-based agent

3. Goal-based agent

4. Utility-based agent

5. Learning agent

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5️⃣ Search Techniques in AI

Search is the heart of AI.

Search Problem Components:

• Initial state

• Goal state

• Operators

• State space

• Path cost

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6️⃣ State Space Search

Definition:

State space is a set of all possible states from initial to goal state.

Example:

8-puzzle problem

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7️⃣ Control Strategies

Control strategy decides:

• Which node to expand next

• How to traverse state space

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8️⃣ Blind Search Techniques

🔹 1. Breadth First Search (BFS)

Characteristics:

• Explores level by level

• Uses Queue (FIFO)

• Finds shortest path

Advantages:

✔ Complete
✔ Optimal

Disadvantages:

❌ High memory usage
❌ Slow for large problems

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🔹 2. Depth First Search (DFS)

Characteristics:

• Goes deep first

• Uses Stack (LIFO)

Advantages:

✔ Less memory
✔ Easy to implement

Disadvantages:

❌ May go into infinite loop
❌ Not optimal

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🔹 BFS vs DFS

BFS	DFS
Uses queue	Uses stack
Complete	Not always
High memory	Low memory
Optimal	Not optimal

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9️⃣ Heuristic Search Techniques

✅ Heuristic

A heuristic is a rule of thumb used to guide the search.

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🔹 1. Hill Climbing

• Moves toward better state

• Uses heuristic function

• Greedy approach

Problems:

• Local maxima

• Plateau

• Ridge

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🔹 2. Best First Search

• Uses evaluation function f(n)

• Chooses best node first

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🔹 3. A* Algorithm

Formula:

$$f(n) = g(n) + h(n)$$

Where:

• g(n) = cost from start to n

• h(n) = heuristic estimate

✔ Complete
✔ Optimal
✔ Widely used

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🔟 AND–OR Graphs & AO* Algorithm

AND–OR Graph

• OR node → choose one path

• AND node → all paths required

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AO* Algorithm

• Used for AND–OR graphs

• Improves A* algorithm

• Finds optimal solution graph

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1️⃣1️⃣ Constraint Satisfaction Problem (CSP)

Definition:

A problem defined by:

• Variables

• Domains

• Constraints

Example:

• N-Queens problem

• Map coloring

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1️⃣2️⃣ Game Playing in AI

Minimax Algorithm

• Two-player game

• MAX vs MIN

• Used in chess

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Alpha-Beta Pruning

• Optimization of minimax

• Reduces nodes explored

• Faster execution

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1️⃣3️⃣ Genetic Algorithms

Inspired by:

• Natural selection

• Evolution

Steps:

1. Initialization

2. Selection

3. Crossover

4. Mutation

5. New generation