A- Search (part 2)

Duration: 3 min

This video lesson is available to enrolled students.

Return to /learn/BIHAR-STET-COMPUTER-SCIENCE-COMPLETE-COURSE/unit-13-artificial-intelligence/search-algorithms/a-search-algorithm/asset-a-search-part-2 after enrolling

Inside: a video lesson and guided study material.

Module outline

  1. About the course: About the Course
  2. Mathematical Aptitude: Number System, HCF LCM, Divisibility Rules, Percentage, Ratio and Proportion (Ratios), Average, Profit and Loss, Time and Work, Mensuration and Geometry, Simple Interest and Compound Interest, Speed Time and Distance, Age Problems, Mixture and Alligation, Algebra, Powers and Exponents (Surds and Indices), Pipe and Cistern, Trigonometry, Statistics, Data Interpretation, Arithmetic
  3. Logical Reasoning: Series (Number and Letter Series) (Numerical Relations and Reasoning), Coding Decoding, Analogy, Blood Relations, Directions (Direction Test), Ranking, Order and Sequence, Non Verbal Reasoning (Spatial Aptitude) (Spatial Reasoning) (Visual Reasoning), Seating Arrangements, Syllogisms, Venn Diagram, Puzzle Solving (Puzzles), Logical Sequence Of Words, Deductive and Inductive Reasoning (Logical Deduction and Induction) (Prepositional Reasoning), Analytical Reasoning (Counting Figures Reasoning), Cubes & Dices, Calendar, Clock
  4. General Knowledge: Physics, Chemistry, Biology, History, Geography, Polity, Indian Economy, GK- Current Affairs, Static General Knowledge
  5. Art of Teaching: Nature, Objectives & Characteristics of Teaching, Learners & Learning Process, Factors Affecting Teaching, Methods of Teaching, Classroom Management & Communication, Teaching-Learning Aids & ICT Integration, Evaluation, Assessment & Measurement, Professional Development & Ethics, Policies, Acts & Contemporary Issues, Curriculum & School Environment, Guidance, Counselling & Mental Health, Inclusive Education & Special Needs, Micro-Teaching & Teaching Skills, Lesson Planning
  6. Environmental Science: Environment
  7. Unit 1 | Digital Electronics: Digital Systems & Boolean Basics, Logic Gates & Hardware, Boolean Expression, Boolean Minimization, Combinational Circuit, Sequential Circuits, Number System, Number Representation
  8. Unit 2 | COA: Basics of COA, Floating Point Rep, Cache Memory Organization, Input Output Organisation, Instr Formats & Modes, Control Unit Design, Register Transfer and Microoperations, Programming the Basic Computer
  9. Unit 3 | C Programming: C Fundamentals, Control Flow, Functions, Arrays & Pointers, Storage Classes, Structures & Enums, DMA, Macros, Scoping & File Handling
  10. Unit 3 | Data Structures: Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing
  11. Unit 4 | Algorithms: Algorithm Analysis, Time Complexity Analysis, Sorting Algorithms, Dynamic Programming, Minimum Spanning Trees, Shortest Path Algos, Advanced Algorithms
  12. Unit 5 | Operating System: Introduction to OS, Process Management, CPU Scheduling, Process Synchronization, Threads & Process Creation, Deadlock, Memory Management, Virtual Memory, Disc Scheduling, File Management
  13. Unit 6 | 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, Transaction Management, Concurrency Control
  14. Unit 7 | Computer Networks: 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
  15. Unit 8 | Software Engineering: Fundamentals of Software Engineering, Software Requirements and Quality Assurance, Software Design, Estimation and Metrics, Software Testing, Software Maintenance and Configuration Management
  16. Unit 9 | Object Oriented Programming: OOPS with C++
  17. Unit 10 | Web Development: HTML Basics, CSS, JavaScript-Basics, XML
  18. Unit 11 | Theory Of Computation: Introduction to TOC, Deterministic FA (DFA), Non-Deterministic FA, Regular Expressions, Grammar, Regular Language Properties, Moore & Mealy Machines, Pushdown Automata & CFG, Turing Machines
  19. Unit 12 | IoT: Emerging Tech Trends, Cloud Computing
  20. Unit 13 | Artificial Intelligence: Approaches to AI, Search Algorithms, Game Playing, Knowledge Representation, Natural Language Processing, Artificial Neural Networks, Genetic Algorithms
  21. Unit 14 | E-Commerce: Electronic Data Interchange and E-Commerce, Role of Internet and Web Services, Security in Mobile and Wireless Computing, Physical Security and Cryptography Basics, Cyber Safety and Security
  22. Unit 15 | Multimedia: Multimedia
  23. Unit 16 | Python: Introduction to Python, Tokens, Variables, Data Types, I/O, Operators & Expressions, Control Flow & Looping, Collections & Strings, Functions & Modules, File Handling & Data Analysis, DSA using Python
  24. Full Length Mock Tests — General Paper:
  25. Full Length Mock Tests — Computer Science (Paper II):
AI summary & chapters

AI Summary

An AI-generated summary of this video lecture.

The lecture introduces A* Search as an informed search algorithm that effectively combines the advantages of both Uniform Cost Search and Greedy Best-First Search. The central focus is the evaluation function f(n) = g(n) + h(n), which guides the search process. The instructor visually demonstrates this mechanism using a tree diagram, calculating the total cost for child nodes to select the optimal path. The second segment provides a formal breakdown of the components, defining g(n) as the actual path cost and h(n) as the heuristic estimate, ultimately defining f(n) as the estimated cost of the cheapest solution through a specific node.

Chapters

  1. 0:00 – 2:00 00:00-02:00

    The instructor begins by presenting a slide that defines A* Search as an informed algorithm combining Uniform Cost Search and Greedy Best-First Search. She writes the core formula f(n) = g(n) + h(n) in red ink on the whiteboard. To illustrate the concept, she draws a search tree with a root node branching into multiple children. She annotates the edges with path costs labeled g(n) and the nodes with heuristic values labeled h(n). She explicitly writes out the summation for the first child node, g(n1) + h(n1), and circles the word min to emphasize that A* selects the node with the lowest total cost. She summarizes the algorithm as UCS + Greedy Best First to reinforce the hybrid nature of the approach.

  2. 2:00 – 3:02 02:00-03:02

    The slide updates to provide a formal definition of the components. It states that g(n) is the path cost from the start node to node n, while h(n) is the estimated cost of the cheapest path from n to the goal. Consequently, f(n) is defined as the estimated cost of the cheapest solution through n. The instructor underlines these definitions and writes A* scales expensive path and on the board, suggesting a discussion on pruning or cost management, though the sentence is incomplete. She circles min again next to the formula to reinforce the selection criteria.

The lesson progresses from a high-level definition of A* Search to a detailed breakdown of its evaluation function. Initially, the instructor establishes that A* is a hybrid of Uniform Cost Search and Greedy Best-First Search. She then derives the formula f(n) = g(n) + h(n) and applies it to a visual tree diagram, showing how path costs and heuristics are summed to determine the next node to expand. The lecture concludes by formally defining g(n) as the actual cost to reach a node and h(n) as the estimated cost to the goal, clarifying that f(n) represents the total estimated cost of a solution path passing through that node.

Loading lesson…