Artificial Intelligence

UNIT I: Introduction to AI

• Definitions and Goals of AI

• AI Approaches and Techniques

• Branches of AI

• Applications of AI

Introduction to Intelligent Systems

• Agents and Environments

• Good Behavior: The Concept of Rationality

• The Nature of Environments

• The Structure of Agents

• How Components of Agent Programs Work

UNIT II: Problem Solving, Search, and Control Strategies

Problem Solving by Searching

• Study and Analysis of Various Searching Algorithms

• Implementation of Depth-First Search

• Problem-Solving Agents

• Searching for Solutions

Uninformed Search Strategies

• Breadth-First Search

• Uniform-Cost Search

• Depth-First Search

• Depth-Limited Search

• Iterative Deepening Depth-First Search

• Bi-Directional Search

Informed (Heuristic) Search Strategies

• Greedy Best-First Search

• A* Search:

  • Minimizing Total Estimated Solution Cost

  • Conditions for Optimality: Admissibility and Consistency

  • Optimality of A*

  • Memory-Bounded Heuristic Search

• Heuristic Functions

• Generating Admissible Heuristics from Sub-Problems: Pattern Databases

• Learning Heuristics from Experience

Beyond Classical Search

• Local Search Algorithms and Optimization Problems:

  • Hill-Climbing Search

  • Simulated Annealing

  • Local Beam Search

  • Genetic Algorithms

• Local Search in Continuous Spaces

• Searching with Non-Deterministic Actions: AND-OR Search Trees

• Searching with Partial Observations

UNIT III: Knowledge Representation Issues and Predicate Logic

Knowledge Representation (KR)

• KR using Predicate Logic

• KR using Rules

Reasoning Systems

• Symbolic Reasoning

• Statistical Reasoning

UNIT IV: Quantifying Uncertainty & Learning Systems

Acting Under Uncertainty

• Basic Probability Notation

• Inference Using Full Joint Distributions

• Bayes' Rule and Its Uses

• Representing Knowledge in Uncertain Domains

• Other Approaches to Uncertain Reasoning

• Rule-Based Methods for Uncertain Reasoning

Representing Vagueness

• Fuzzy Sets and Fuzzy Logic

• Study of Fuzzy Logic

• Decision Trees

• Implementation Aspects of Decision Trees

Learning from Examples

• Forms of Learning

• Supervised Learning

• Learning Decision Trees

  • Decision Tree Representation

  • Expressiveness of Decision Trees

  • Inducing Decision Trees from Examples

UNIT V: Expert Systems

Introduction to Expert Systems

• Knowledge Acquisition

• Knowledge Base

• Working Memory

• Inference Engine

• Expert System Shells

• Explanation

• Applications of Expert Systems

Fundamentals of Neural Networks

• Introduction and Research History

• Model of Artificial Neuron

• Characteristics of Neural Networks

• Learning Methods in Neural Networks

• Single-Layer Neural Network System

• Applications of Neural Networks

Fundamentals of Genetic Algorithms

• Introduction

• Encoding

• Operators of Genetic Algorithms

• Basic Genetic Algorithm