This course studies four main objectives of AI. Modeling the environment by constructing computer representations of the real world. Perception and reasoning - obtaining and creating information/knowledge to populate a computational representation. Taking actions by using the knowledge of the environment and desired goals to plan and execute actions. Learning from past experience.
Things you will cover in this course:
- Artificial Intelligence:
- Introduction,
- Intelligent Agents.
- Problem-solving:
- Solving Problems by Searching,
- Informed Search and Exploration,
- Constraint Satisfaction Problems,
- Adversarial Search.
- Knowledge and reasoning:
- Logical Agents,
- First-Order Logic,
- Inference in First-Order Logic,
- Knowledge Representation.
- Planning and Acting in the Real World.
- Uncertain knowledge and reasoning:
- Uncertainty, Probabilistic Reasoning,
- Probabilistic Reasoning over Time,
- Making Simple Decisions,
- Making Complex Decisions.
- Learning: Learning from Observations, Knowledge in
- Learning, Statistical Learning Methods, Reinforcement
- Learning. Communicating, perceiving, and acting:
- Communication,
- Probabilistic Language Processing,
- Perception and Robotics.
- Introduction to LISP/PROLOG and Expert Systems (ES) and Applications
Reference material:
1. “Artificial Intelligence: Structures and Strategies for Complex Problem Solving”, George F. Luger, 6th edition: Pearson Education, 2008.2. “Artificial Intelligence: A Modern Approach”, Stuart Jonathan Russell, Peter Norvig, John F. Canny, 2nd Edition, Prentice Hall, 2003.
0 Comments