At the end of this course, students will be able to:
- Express data and knowledge using logical models and knowledge representation schemes.
- Apply logical principles for sound reasoning to prove theorems.
- Analyze knowledge rich domains to formulate heuristics.
- Understand algorithms for uninformed, informed and local search, game playing, and constraint satisfaction problems, and develop efficient solutions.
- Analyze real-life applications to represent uncertain knowledge with the help of probability theory.
- Investigate the use of Bayesian Networks and Partially Observable / Markov Decision Processes (PO/MDP) for probabilistic rational reasoning.
- Expose themselves to major usage areas of artificial intelligence algorithms in the field of robotics.
- Use a wide variety of artificial intelligence techniques and tools that are covered in the course.
- Evaluate and compare AI algorithms in terms of time and space complexity, completeness and optimality.
- Design and implement AI software solutions to realistic problems.