Artificial Intelligence for Robotics Robotics & Automation 7th Sem Syllabus for BE 2017 Regulation Anna Univ (Professional Elective III) detail syllabus for Robotics And Automation Engineering (Robotics & Automation), 2017 regulation is collected from the Anna Univ official website and presented for students of Anna University. The details of the course are: course code (RO8003), Category (PE), Contact Periods/week (3), Teaching hours/week (3), Practical Hours/week (0). The total course credits are given in combined syllabus.
For all other robotics & automation 7th sem syllabus for be 2017 regulation anna univ you can visit Robotics & Automation 7th Sem syllabus for BE 2017 regulation Anna Univ Subjects. For all other Professional Elective III subjects do refer to Professional Elective III. The detail syllabus for artificial intelligence for robotics is as follows.
Course Objective:
The student should be made to:
- Study the concepts of Artificial Intelligence.
- Learn the methods of solving problems using Artificial Intelligence.
- Introduce the concepts of Expert Systems and machine learning.
- Learn about planning and reasoning artificial intelligence.
- Solve the risk in artificial intelligence.
Unit I
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Unit II
Planning
Planning withforward and backward State space search – Partial order planning – Planning graphs-Planning with propositional logic – Planning and acting in real world.
Unit III
Reasoning:
Uncertainity – Probabilistic reasoning-Filtering and prediction-Hidden Markov models-Kalman filters-Dynamic Bayesian Networks, Speech recognition, making decisions.
Unit IV
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Unit V
Ai in Robotics:
Robotic perception, localization, mapping- configuring space, planning uncertain movements, dynamics and control of movement, Ethics and risks of artificial intelligence in robotics.
Course Outcome:
At the end of the course, the student should be able to:
- Identify problems that are amenable to solution by AI methods.
- Identify appropriate AI methods to solve a given problem.
- Formalise a given problem in the language/framework of different AI methods.
- Implement basic AI algorithms.
- Design and carry out an empirical evaluation of different algorithms on a problem formalisation, and state the conclusions that the evaluation supports.
Text Books:
- Stuart Russell, Peter Norvig, Artificial Intelligence: A modern approch, Pearson Education, India2003.
- Negnevitsky, M, Artificial Intelligence: A guide to Intelligent Systems,. Harlow: Addison-Wesley, 2002.
References:
- David Jefferis, Artificial Intelligence: Robotics and Machine Evolution, Crabtree Publishing Company, 1992.
For detail syllabus of all other subjects of BE Robotics & Automation, 2017 regulation do visit Robotics & Automation 7th Sem syllabus for 2017 Regulation.
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