Search Strategies

Duration: 7 min

This video lesson is available to enrolled students.

Return to /learn/ISRO/ai-machine-learning-big-data-cloud/artificial-intelligence-approaches-to-ai/search-strategies-in-ai/asset-search-strategies after enrolling

Inside: a video lesson and guided study material.

Module outline

  1. About Course: About the Course
  2. General Aptitude & Reasoning: Number System, Percentage, Ratio and Proportion (Ratios), Average, Profit and Loss, Simple Interest and Compound Interest, Time and Work, Speed Time and Distance, Mixture and Alligation, Data Interpretation, Algebra, Probability, Permutation and Combination, Venn Diagram, HCF LCM, Divisibility Rules, Age Problems, Pipe and Cistern, Partnership, Linear Algebra, Sequence and Series (Series), Statistics, Powers and Exponents (Surds and Indices), Arithmetic, Mensuration and Geometry, Calculus, Trigonometry, Vectors, Speed Mathematics and Basic Mathematics, Non Verbal Reasoning (Spatial Aptitude) (Spatial Reasoning) (Visual Reasoning), Analogy, Series (Number and Letter Series) (Numerical Relations and Reasoning), Puzzle Solving (Puzzles), Syllogisms, Deductive and Inductive Reasoning (Logical Deduction and Induction) (Prepositional Reasoning), Calendar, Seating Arrangements, Coding Decoding, Blood Relations, Directions (Direction Test), Clock, Analytical Reasoning (Counting Figures Reasoning), Ranking, Order and Sequence, Logical Sequence Of Words, Inequality, Data Sufficiency, Cubes & Dices
  3. Computer Networks & Cybersecurity: Introduction to CN, Data Communication, DLL: Access Control, DLL: Flow Control, DLL: Error Control, DLL: Framing, Data Link Layer - Ethernet, Net Layer: IPv4 & Proto, Net Layer: IP Addressing, Net Layer:Routing Protocol, Transport Layer Services, TL: Congestion & UDP, Application Layer, Hardware Basics, Network Security
  4. Programming, Data Structures & Algorithms: C Fundamentals, Control Flow, Functions, Arrays & Pointers, Storage Classes, Structures & Enums, DMA, Macros, Scoping & File Handling, OOPS with C++, Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing, Algorithm Analysis, Time Complexity Analysis, Sorting Algorithms, Greedy Algorithms, Dynamic Programming, Minimum Spanning Trees, Shortest Path Algos, Advanced Algorithms
  5. AI, Machine Learning, Big Data & Cloud: Emerging Tech Trends, Artificial Intelligence, Big Data Systems, Approaches to AI, Search Algorithms, Knowledge Representation, Cloud Computing
  6. Digital Logic & Computer Organization: Digital Systems & Boolean Basics, Logic Gates & Hardware, Boolean Expression, Boolean Minimization, Combinational Circuit, Sequential Circuits, Number System, Number Representation, Basics of COA, Floating Point Rep, Cache Memory Organization, Input Output Organisation, Pipelining, Instr Formats & Modes, Register Transfer and Microoperations, Control Unit Design, Programming the Basic Computer
  7. TOC & Compiler: Introduction to TOC, Deterministic FA (DFA), Non-Deterministic FA, Regular Expressions, Grammar, Regular Language Properties, Moore & Mealy Machines, Pushdown Automata & CFG, Turing Machines, Complexity Theory, Intro to Compilers, Lexical Analysis, Grammar & CFG, Syntax Analysis: Top-Down, Syntax Analysis: Bottom-Up, Semantic Analysis & SDT, Intermediate Code Gen, Code Optimization, Run Time Environment
  8. Discrete Mathematics: Set Theory, Relations, Functions, Graph Theory, Group Theory, Propositional and Predicate Logic
  9. Operating System: Introduction to OS, Process Management, CPU Scheduling, Process Synchronization, Threads & Process Creation, Deadlock, Memory Management, Virtual Memory, Disc Scheduling, File Management, Unix OS, Linux OS, Distributed Systems
  10. Software Engineering: Fundamentals of Software Engineering, Software Requirements and Quality Assurance, Software Design, Estimation and Metrics, Software Testing, Software Maintenance and Configuration Management, Object Oriented Technology, HTML Basics, JavaScript-Basics, Java, XML
  11. Database Management System: Basics of DBMS, ER Diagram, Relational Model & Functional Dependencies, Keys & Integrity Constraints, Normalization (1NF - BCNF), Decomposition Properties & 4NF, File Organization & Indexing, Relational Algebra, SQL, Relational Calculus, Transaction Management, Concurrency Control, Database Recovery, Data Warehousing & Mining, Enhanced Data Models, NoSQL
  12. Other Recurring CS Topics: Basics of Computer Graphics, 2-D Geometrical Transforms and Viewing, Multimedia
  13. Live Classes: ISRO Live classes
  14. Full Mock Test:
AI summary & chapters

AI Summary

An AI-generated summary of this video lecture.

The video provides a comprehensive introduction to search strategies within the field of Artificial Intelligence. It begins by establishing the fundamental components of a search problem, specifically defining the 'Initial State' and 'Goal State' using a tic-tac-toe game as a visual aid. The instructor illustrates how a search algorithm navigates from the start to the finish through various 'Possibilities' and 'Possible paths.' The lecture then categorizes search algorithms into two primary types: 'Uninformed' (or blind) search and 'Informed' (or heuristic) search. Finally, the video details the critical parameters for evaluating the performance of these search techniques, including completeness, time and space complexity, and optimality, before diving into the specifics of uninformed search strategies.

Chapters

  1. 0:00 – 2:00 00:00-02:00

    The instructor begins the lecture by writing 'Initial State' and 'Goal State' on a digital whiteboard to define the boundaries of a search problem. She uses a tic-tac-toe grid to visualize the concept of a state space, drawing arrows to represent the 'Possible path' from the beginning to the end. Key terms such as 'Search Algorithm,' 'Possibilities,' and 'Utility function' are written on the screen to set the context for how a computer navigates a problem space. She also writes 'Compute problem' to indicate the computational nature of finding a solution.

  2. 2:00 – 5:00 02:00-05:00

    The lesson progresses to 'State Space Representation,' which is defined as the combination of all states and possible actions. The instructor draws a tree structure to visualize the branching nature of search, showing how nodes expand. She categorizes 'Search AI' into two main branches: 'Uninformed' (also called 'Blind') and 'Informed' (or 'Heuristic Search'). Under uninformed search, she lists specific algorithms like 'Breadth,' 'Depth,' and 'Shallow,' while also introducing evaluation parameters such as 'Completeness,' 'Time and Space Complexity,' and 'Optimality.' She writes 'Search Strategies' and underlines the importance of these metrics.

  3. 5:00 – 6:46 05:00-06:46

    The focus shifts to evaluating search algorithms in detail. The instructor writes 'Search Algorithm -> Complete' and 'Optimal (Best)' to emphasize performance metrics. She draws a tree diagram to further illustrate the search process and writes 'Time Complex' and 'Space' to denote resource usage. The video then transitions to a PDF slide titled 'Search Strategies,' where she highlights 'Uninformed Search (Brute Force Search/Blind Search/Exhaustive Search).' She explains that these strategies explore the search space systematically without specific information or heuristics, often by selecting nodes at random or in a predetermined order.

The video provides a structured introduction to search strategies in AI, moving from basic definitions to complex evaluation criteria. It starts with the conceptual foundation of states and goals, using visual aids like tic-tac-toe grids to make abstract ideas concrete. The lecture then classifies search algorithms into informed and uninformed types, listing specific examples for each. Finally, it details the specific metrics used to judge their effectiveness, such as completeness and optimality. The visual aids, including tree diagrams and PDF slides, help clarify abstract concepts like state space and branching factors, providing a solid foundation for understanding how AI systems solve problems.

Loading lesson…