Welcome to Introduction to Artificial Intelligence
Goal:
This course covers the fundamentals of
artificial Intelligence and the use of logic programming language to solve AI
related problems.
Important things should be known after studying this course:
* Define
AI.
* Discuss
task problems, techniques and limitations of AI.
* Discuss
how an agent acts in an environment.
* Use
Uninformed search and Informed search methods for problem solving for single
agent
including Breadth First Search, Depth First Search, Depth Limited Search, Greedy Search .
including Breadth First Search, Depth First Search, Depth Limited Search, Greedy Search .
* Use
the game playing methodology and search game tree using Minimax and Alpha-Beta
pruning algorithms for solution.
pruning algorithms for solution.
* Construct
constraint graph and use techniques for solving CSPs such as Backtrackin
algorithm.
algorithm.
* Evaluate
natural language sentences using propositional logic and first order logic
(FOL) as a
knowledge representation language.
knowledge representation language.
* Evaluate
entailment sentences.
* Create
Inferences in forms of Propositional Logic and First Order Logic Knowledge
base.
* Make
use of logic programming languages such as Prolog and Lisp.
* Use
formal languages for planning such as STRIPS and create plan execution and
action
schema.
schema.
* Use
of probability theory to represent uncertainty.
* Construct
a Bayesian network for a given problems.
* Use
Hidden Markov models in order to solve fuzziness.
* Discuss
the concepts of machine learning and create Decision-Tree.
* Discuss
the concepts of robotics.
IT Society blog Team
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