📘 IAI

INTRODUCTION TO ARTIFICIAL INTELLIGENCE

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IAI Unit 1: AI and Agents

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IAI Unit 2: Games And Knowledge-based Agents

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IAI Unit 3: First-order Logic And Knowledge Representation

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IAI Unit 5: Probabilistic Reasoning

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IAI Unit 1 (alt): AI and Agents

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IAI Unit 4 (alt): Planning

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IAI Unit 5 (alt): Probabilistic Reasoning

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IAI Mid 1 Quesetion and Answers

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IAI Mid 2 Quesetion and Answers

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Syllabus Overview

UNIT - 1: Introduction to AI and Problem-Solving Agents

Introduction to AI - Intelligent Agents

  • Problem-Solving Agents

Searching for Solutions

  • Breadth-first search
  • Depth-first search
  • Hill-climbing search
  • Simulated annealing search
  • Local Search in Continuous Spaces

UNIT - 2: Games and Knowledge-Based Agents

Games

  • Optimal Decisions in Games
  • Alpha–Beta Pruning

Constraint Satisfaction Problems (CSPs)

  • Defining Constraint Satisfaction Problems
  • Constraint Propagation
  • Backtracking Search for CSPs

Knowledge-Based Agents

  • Logic
  • Propositional Logic
  • Propositional Theorem Proving: Inference and proofs
  • Proof by resolution
  • Horn clauses and definite clauses

UNIT - 3: First-Order Logic and Knowledge Representation

First-Order Logic

  • Syntax and Semantics of First-Order Logic
  • Using First Order Logic
  • Inference in First-Order Logic: Propositional vs. First-Order Inference
  • Unification
  • Forward Chaining
  • Backward Chaining
  • Resolution

Knowledge Representation

  • Ontological Engineering
  • Categories and Objects
  • Events

UNIT - 4: Planning

Planning

  • Definition of Classical Planning
  • Algorithms for Planning with State Space Search
  • Planning Graphs
  • Other Classical Planning Approaches
  • Analysis of Planning approaches
  • Hierarchical Planning

UNIT - 5: Probabilistic Reasoning

Probabilistic Reasoning

  • Acting under Uncertainty
  • Basic Probability Notation
  • Bayes’ Rule and Its Use
  • Representing Knowledge in an Uncertain Domain
  • The Semantics of Bayesian Networks
  • Efficient Representation of Conditional Distributions
  • Approximate Inference in Bayesian Networks
  • Relational and First-Order Probability
INTRODUCTION TO ARTIFICIAL INTELLIGENCE Notes