Quantifying Uncertainity

Duration: 8 min

This video lesson is available to enrolled students.

Return to /learn/BIHAR-STET-COMPUTER-SCIENCE-COMPLETE-COURSE/unit-13-artificial-intelligence/artificial-intelligence-knowledge-representation/expert-systems-and-uncertainty/asset-quantifying-uncertainity 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.

This lecture segment introduces the critical challenge of quantifying uncertainty in artificial intelligence agents. The instructor explains that agents often operate in environments characterized by partial observability or non-determinism, meaning they cannot always be certain of their current state or the outcome of their actions. The session details how problem-solving and logical agents attempt to manage this by maintaining a belief state, which represents the set of all possible world states. The lecture then transitions to a brief overview of Expert System Technology, outlining the components of development environments and the programming languages typically used.

Chapters

  1. 0:00 – 2:00 00:00-02:00

    The video begins with a slide titled QUANTIFYING UNCERTAINTY. The instructor underlines the word uncertainty and writes it on the screen. She explains that agents may need to handle uncertainty due to partial observability or non-determinism. She writes predictable next to the title but seems to correct or modify the thought. She draws a simple diagram of an agent interacting with an environment, labeling the box enviro and noting that sensors are noisy. This visual aid helps illustrate how an agent perceives the world imperfectly. She emphasizes that an agent may never know for certain what state it is in or where it will end up after a sequence of actions.

  2. 2:00 – 5:00 02:00-05:00

    The instructor focuses on how problem-solving and logical agents handle this uncertainty. She highlights the term belief state on the slide, defining it as a representation of the set of all possible world states that the agent might be in. She writes uncertainty and State Space on the screen, linking the concept to the set of all possible states. The lecture then moves to the drawbacks of this approach. She highlights contingency plan and explains that agents must generate plans that handle every possible eventuality. She writes B. huge to describe the resulting belief-state representations, noting they can become impossible to manage. She also mentions that a correct contingent plan can grow arbitrarily large and must consider unlikely contingencies.

  3. 5:00 – 8:18 05:00-08:18

    The slide changes to Expert System Technology. The instructor lists several levels of ES technologies available. She discusses the Expert System Development Environment, which includes hardware like workstations, minicomputers, and mainframes. She also mentions high-level symbolic programming languages such as LISP and PROLOG, writing 7 python on the screen, likely suggesting Python as a modern alternative or addition. She notes that these environments include large databases and tools that reduce the effort and cost involved in developing an expert system. The section concludes by emphasizing the role of tools in simplifying the development process.

The lecture effectively bridges the gap between theoretical AI concepts and practical system design. It starts by defining the problem of uncertainty, showing how agents use belief states to navigate unknown environments. It then critically analyzes the limitations of logical agents, specifically the computational cost of maintaining large belief states. Finally, it shifts to the practical implementation of AI systems through Expert Systems, highlighting the necessary hardware, software, and tools required to build them. This progression from abstract problem definition to concrete technological solutions provides a comprehensive overview of the subject matter, preparing students for deeper study in AI architecture.

Loading lesson…