Greedy BFS

Duration: 2 min

This video lesson is available to enrolled students.

Return to /learn/MPPSC-ASSISTANT-PROFESSOR-COMPUTER-SCIENCE-PAPER-2/current-trends-technologies/search-algorithms/best-first-search/asset-greedy-bfs after enrolling

Inside: a video lesson and guided study material.

Module outline

  1. About Course: About the Course
  2. Paper 1 | General Studies: State Wise GK
  3. Paper 1 | Information and Communication Technology: Role of Internet and Web Services, Security in Mobile and Wireless Computing
  4. Paper 1 | Current Affairs: GK- Current Affairs
  5. Paper 2 | Discrete Mathematical Structure: Digital Systems & Boolean Basics, Logic Gates & Hardware, Boolean Expression, Boolean Minimization, Set Theory, Relations, Functions, Graph Theory, Group Theory, Propositional and Predicate Logic, Probability
  6. Paper 2 | Digital Computer Organization And Architecture: Number System, Number Representation, Sequential Circuits, Combinational Circuit, 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. Paper 2 | C Programming: C Fundamentals, Control Flow, Functions, Arrays & Pointers, Storage Classes, Structures & Enums, DMA, Macros, Scoping & File Handling
  8. Paper 2 | Programming Languages & Object-Oriented Programming Concepts: Java, OOPS with C++, Java Fundamentals, Array, String, IO & References
  9. Paper 2 | Python: Introduction to Python, Tokens, Variables, Data Types, I/O, Operators & Expressions, Control Flow & Looping, Collections & Strings, Functions & Modules, File Handling & Data Analysis
  10. Paper 2 | Data Structure And Algorithms: Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing, Algorithm Analysis, Time Complexity Analysis, Sorting Algorithms, Greedy Algorithms, Minimum Spanning Trees, Advanced Algorithms
  11. Paper 2 | Operating System: Introduction to OS, Process Management, CPU Scheduling, Process Synchronization, Deadlock, Threads & Process Creation, Memory Management, Virtual Memory, Disc Scheduling, File Management, Unix OS
  12. Paper 2 | 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, Complexity Theory
  13. Paper 2 | Compiler Design: 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
  14. Paper 2 | Database Management System: Basics of DBMS, ER Diagram, Relational Model & Functional Dependencies, Keys & Integrity Constraints, Normalization (1NF - BCNF), Decomposition Properties & 4NF, Relational Algebra, SQL, Transaction Management, Concurrency Control, Database Recovery, Database Security & Authorization, Enhanced Data Models
  15. Paper 2 | Computer Network And Information Security: Introduction to CN, Data Communication, Application Layer, Hardware basics, Cyber Threats and Malware Attacks, Network Security, 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
  16. Paper 2 | Software Engineering: Fundamentals of Software Engineering, Software Requirements and Quality Assurance, Software Design, Estimation and Metrics, Software Testing, Software Maintenance and Configuration Management
  17. Paper 2 | Current Trends & Technologies: Emerging Tech Trends, Approaches to AI, Search Algorithms, Game Playing, Knowledge Representation, Planning, Multi Agent Systems, Fuzzy Sets, Natural Language Processing, Artificial Neural Networks, Genetic Algorithms, Artificial Intelligence, Data Warehousing & Mining, Electronic Data Interchange and E-Commerce, Terms & Abbreviations
  18. Paper 2 | Full Length Mock Tests:
AI summary & chapters

AI Summary

An AI-generated summary of this video lecture.

The video lecture introduces Greedy Best-first Search, defining it as an algorithm that always attempts to continue with the node that seems closest to the goal. The instructor distinguishes this from A* search by writing the evaluation function f(n) = h(n), indicating it relies solely on the heuristic. A diagram with nodes A, B, C, and D illustrates a scenario where the search might fail to find the best path. The lecture explicitly states that Greedy Best-first Search is neither optimal nor complete, similar to Depth-First Search. The session concludes by listing the algorithm's properties: it is not complete due to potential loops (e.g., Iasi -> Neamt -> Iasi), has a time complexity of O(b^m), a space complexity of O(b^m) because it keeps all nodes in memory, and is not optimal, demonstrated by a path cost comparison where the found path cost 450 versus an optimal cost of 418.

Chapters

  1. 0:00 – 1:38 00:00-01:38

    The video begins by defining Greedy Best-first Search as prioritizing nodes closest to the goal, writing the formula f(n) = h(n) to show it ignores path cost. A diagram with nodes A, B, C, and D illustrates a graph where the search might get stuck or take a longer route, leading to the text stating the method is 'not optimal' and 'not complete.' The instructor underlines these key limitations. The slide then transitions to a list of properties, detailing that the algorithm is not complete because it can get stuck in loops like Iasi -> Neamt -> Iasi. It lists time complexity as O(b^m) and space complexity as O(b^m), noting that a good heuristic can improve time but space remains high. Finally, it confirms the algorithm is not optimal, providing a specific example where a found path cost 450, while a better path (Arad to Bucharest) cost 418.

The lesson progresses from a conceptual definition of Greedy Best-first Search to its practical limitations and formal properties. By comparing it to A* and using a visual graph, the instructor highlights why relying solely on the heuristic h(n) leads to suboptimal solutions and potential infinite loops. The final slide consolidates these findings into standard search algorithm metrics (completeness, time, space, optimality), providing specific numerical examples to reinforce the theoretical claims.

Loading lesson…