Course Code:
CEID_ΝΥ451
Type:
Semester:
Division:
Instructors:
Credit Points:
6
- Introduction to Artificial Intelligence
- Introduction to problem solving theory
- Search space, problem modeling, constraints, problem solving
- Basic concepts (representation, goal, evaluation function, definition of search problem, neighboring areas and local optimal points, hill climbing methods)
- Traditional Methods - Part I (exhaustive search, local search)
- Traditional Methods - Part II (depth-first and breadth-first search, greedy algorithms, algorithm A *, general search algorithm, dynamic programming)
- Solving Constraint Satisfaction Problems
- Knowledge Representation (Definition, Key Elements, Evaluation Criteria, Procedural and Declarative View)
- First-order predicate logic, Basic concepts of model theory and proof theory, Clausal form, Resolution Principle, Resolution Refutation, Resolution Strategies
- Logical Programming, Prolog Language
- Rules and Production Systems
- Representation of uncertain knowledge (Bayes rules, certainty factors)
- Semantic networks, Frames
- Planning
- Intelligent agents