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Artificial Intelligence Search Methods For Problem Solving

Artificial Intelligence Search Methods For Problem Solving banner
Preview this course
Self-paced Beginner

Artificial Intelligence Search Methods For Problem Solving

4(1579)
16 enrolled
2438 views
FREE
2344 min
Anytime
English
Team EveryEng
Team EveryEngMechanical Engineering
AerospaceAutomotive
  • Lifetime access
  • Certificate of completion
  • Foundational Learning
  • Access to Study Materials
Volume pricing for groups of 5+

Why enroll

Completing the NPTEL course "Artificial Intelligence Search Methods For Problem Solving" can significantly enhance your career prospects in AI and related fields. By mastering search methods and problem-solving strategies, you'll develop a strong foundation in AI and be able to tackle complex challenges. This expertise will open doors to roles like AI/ML Engineer, Data Scientist, and Problem-Solving Specialist. You'll also gain a competitive edge in industries like robotics, healthcare, finance, and more. With this course, you'll be well-equipped to drive innovation and solve real-world problems, leading to accelerated career growth and new opportunities.

Your instructor

Team EveryEng

Team EveryEng

Engineer

Mechanical Engineering

22+ yrs experience·India

150Courses
43Articles
4.6kLearners
4Avg rating
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Is this course for you?

You should take this if

  • You work in Aerospace or Automotive
  • You're a Mechanical Engineering professional
  • You prefer self-paced learning you can revisit

You should skip if

  • You need a different specialisation outside Mechanical Engineering
  • You need live interaction with an instructor

Course details

This course provides a comprehensive understanding of fundamental search methods used in Artificial Intelligence (AI) for solving complex and real-world problems. It introduces students to a wide range of search strategies, including uninformed search techniques such as breadth-first and depth-first search, as well as informed methods like heuristic-based search and A* algorithms. Learners will also explore local search techniques used in optimization problems and gain insights into constraint satisfaction problems (CSPs). The course emphasizes both theoretical concepts and practical implementation, helping students understand how different search algorithms work and when to apply them. Through examples and hands-on exercises, participants will develop the ability to model problems effectively and design efficient solution strategies. Additionally, the course highlights performance evaluation, complexity analysis, and optimization of search processes. By the end of the course, students will be equipped with the knowledge and skills to apply AI search methods in domains such as robotics, game development, and decision-making systems.

Source: nptelhrd (Youtube Channel)
Artificial Intelligence by Prof. Deepak Khemani,Department of Computer Science and Engineering,IIT Madras.

Course suitable for

Key topics covered

  • Artificial Intelligence: Introduction

  • Introduction to AI

  • AI Introduction: Philosophy

  • Introduction: Philosophy

  • State Space Search - Introduction

  • Search - DFS and BFS

  • Search DFID

  • Heuristic Search

  • Hill climbing

  • Solution Space Search,Beam Search

  • TSP Greedy Methods

  • Tabu Search

  • Optimization - I (Simulated Annealing)

  • Optimization II (Genetic Algorithms)

  • Population based methods for Optimization

  • Population Based Methods II

  • Branch and Bound, Dijkstra's Algorithm

  • A* Algorithm

  • Admissibility of A*

  • A* Monotone Property, Iterative Deeping A*

  • Recursive Best First Search, Sequence Allignment

  • Pruning the Open and Closed lists

  • Problem Decomposition with Goal Trees

  • AO* Algorithm

  • Game Playing

  • Game Playing- Minimax Search

  • Game Playing - AlphaBeta

  • Game Playing-SSS *

  • Rule Based Systems

  • Inference Engines

  • Rete Algorithm

  • Planning

  • Planning FSSP, BSSP

  • Goal Stack Planning Sussman's Anomaly

  • Non-linear planning

  • Plan Space Planning

  • GraphPlan

  • Constraint Satisfaction Problems

  • CSP Continued

  • Knowlege Based Systems

  • Knowlege Based Systems PL

  • Propositional Logic

  • Resolution Refutation for PL

  • First Order Logic (FOL)

  • Reasoning in FOL

  • Backward Chaining

  • Resolution for FOL

Course content

The course is readily available, allowing learners to start and complete it at their own pace.

48 lectures39 hr 4 min

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