{"id":28964,"date":"2025-04-14T14:36:19","date_gmt":"2025-04-14T09:06:19","guid":{"rendered":"https:\/\/www.inspirenignite.com\/mh\/314333-ai-in-robotics-syllabus-for-automation-robotics-4th-sem-k-scheme-msbte-pdf\/"},"modified":"2025-04-14T14:36:19","modified_gmt":"2025-04-14T09:06:19","slug":"314333-ai-in-robotics-syllabus-for-automation-robotics-4th-sem-k-scheme-msbte-pdf","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/mh\/314333-ai-in-robotics-syllabus-for-automation-robotics-4th-sem-k-scheme-msbte-pdf\/","title":{"rendered":"314333: Ai in Robotics Syllabus for Automation & Robotics 4th Sem K Scheme MSBTE PDF"},"content":{"rendered":"

Ai in Robotics detailed Syllabus for Automation & Robotics (AO), K scheme PDF has been taken from the MSBTE<\/a> official website and presented for the diploma students. For Subject Code, Subject Name, Lectures, Tutorial, Practical\/Drawing, Credits, Theory (Max & Min) Marks, Practical (Max & Min) Marks, Total Marks, and other information, do visit full semester subjects post given below. <\/p>\n

For all other MSBTE Automation & Robotics 4th Sem K Scheme Syllabus PDF, do visit MSBTE Automation & Robotics 4th Sem K Scheme Syllabus PDF Subjects<\/a>. The detailed Syllabus for ai in robotics is as follows.<\/p>\n

Rationale<\/h4>\n

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App<\/a>
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.<\/b>
\"Get<\/a>.<\/p>\n

Course Outcomes:<\/h4>\n

Students will be able to achieve & demonstrate the following COs on completion of course based learning<\/p>\n

    \n
  1. Determine initial state and goal state for a given problem.<\/li>\n
  2. Use relevant AI search strategies for problem solving.<\/li>\n
  3. Interpret different types of knowledge and reasoning techniques used in AI.<\/li>\n
  4. Apply the learning methods adopted in AI.<\/li>\n
  5. Apply the principles of AI in robotics.<\/li>\n<\/ol>\n

    Unit I<\/h4>\n

    Basics of Artificial Intelligence 1.1\tAI: Definition and characteristics, history, scope, need for AI in Robotics 1.2\tAgent and environment: Definition, characteristics and classification of agents, rational agent and intelligent agent, environment and its properties 1.3\tState space search: Goal directed agent, State space search notations-Initial state, action or an operator, plan, path cost 1.4\tAI Ethics: Transparency, fairness, accountability, privacy, security
    \nLecture usin chalk and bo Presentation\n<\/p>\n

    Unit II<\/h4>\n

    For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App<\/a>
    It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.<\/b>
    \"Get<\/a>.<\/p>\n

    Unit III<\/h4>\n

    Knowledge, Reasoning and Planning 3.1\tKnowledge: Definition and types of knowledge 3.2\tKnowledge representation techniques, AI knowledge life cycle 3.3\tKnowledge based agent in AI: Introduction, architecture, rules of inference, first order logic, forward chaining and backward chaining 3.4\tReasoning: Definition and its types, forward reasoning and backward reasoning, probabilistic reasoning: need, cause of uncertainty, bayesian reasoning 3.5\tPlanning: Definition, types of planning, planning graph
    \nLecture usin chalk and bo Presentation Demonstrati\n<\/p>\n

    Unit IV<\/h4>\n

    Learning adopted in AI 4.1\tForms of learning, knowledge in learning, statistical learning methods, Importance of AI in learning 4.2\tMachine learning: Definition, techniques in machine learning – supervised learning, unsupervised learning, reinforcement learning, semi-supervised learning 4.3\tIntroduction to predictive modeling: definition, stages of predictive modeling – problem definition, hypothesis generation, data extraction\/collection, data exploration and transformation, splitting dataset into training set and test set, types of predictive models, algorithms of predictive modelling 4.4\tCommunication, perceiving and acting, probabilistic language processing and perception
    \nLecture usin chalk and bo Presentation Demonstrati Flipped Classroom\n<\/p>\n

    Unit V<\/h4>\n

    For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App<\/a>
    It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.<\/b>
    \"Get<\/a>.<\/p>\n

    List of Experiments:<\/h4>\n
      \n
    1. * Identification of initial state and goal state for a given 3-pegs problem\t2\tC<\/li>\n
    2. Identification of initial state and goal state for a given problem on chessboard\t2\tC<\/li>\n
    3. * Implementation of breadth first search algorithm\t2\tC<\/li>\n
    4. Implementation of depth first search algorithm\t2\tC<\/li>\n
    5. * Implementation of forward chaining algorithm\t2\tC<\/li>\n
    6. Implementation of travelling salesman problem\t2\tC<\/li>\n
    7. * Implementation of forward chaining algorithm\t2\tC<\/li>\n
    8. Implementation of backward chaining algorithm\t2\tC<\/li>\n
    9. Implementation of forward reasoning\t2\tC<\/li>\n
    10. * Implementation of backward reasoning\t2\tC<\/li>\n
    11. Implementation of Bayesian reasoning\t2\tC<\/li>\n
    12. Implementation of data extraction\t2\tC<\/li>\n
    13. * Develop a program to split dataset\t2\tC<\/li>\n
    14. * Implementation of motion commands for robot using simulator\t2\tC<\/li>\n
    15. Implementation of end effector command for a given robot\t2\tC<\/li>\n
    16. Execution of robotic operations by bridging robotvision systems\t2\tC<\/li>\n
    17. * Implementation of specific path movement of robot\t2\tC<\/li>\n
    18. Implementation of painting operation with AI based robot\t2\tC<\/li>\n<\/ol>\n

      Self Learning<\/h4>\n<\/p>\n

      Micro Project<\/i><\/p>\n