This course introduces the students the basic concepts of the Artificial Intelligence. The focus of attention of this course will be Techniques for Problem Solving & Control strategies for developing Intelligent Systems. Logic-Based Knowledge Representation and an introductory course for Programming in Logic (PROLOG) will be also a central part of this course.
Discussing whether machine are capable of thinking or not?
Understanding Turing Test and Success Criteria
Discussing the Role of Heuristics
Analysing human behaviour and intelligent behaviour
Learning the different applications of AI technology
The AI-projects are cindered in this course is to give the students a practical overview of AI applications
Learning in teamwork how to implement and analyse an AI topic. For more details see also AI project below
Explaining the architecture of an agent
Understanding the difference between agents that are capable of keeping track with their environments
Explaining the applications of the agent approach
Understanding how to describe a problem in terms of space, start state, transitions rules and goal state
Discussing the meaning of the combinatorial explosion when searching for a goal
Using an uniformed Search technique by solving a problem und describing the problems involved in such Solutions
Solving the same problem using informed search based on heuristics
Understanding how to implement a heuristic function capable of finding the optimal solution path in the search space
Learning how to utilize MINIMAX search with alpha-beta pruning for some two-player game
Utilizing Backtracking for Solving a Constraint satisfaction Problem
Understanding how represent knowledge using logical Formulas and prepositional logic
Understanding the basic concepts how to utilize resolution and unification as theorem proving method
Understanding the shortcoming of two valued logic
Understanding basic principles of model theory
Understanding Recursive Search, Backtracking in PROLOG and Backward Chaining and Theorem Proving in PROLOG
Learning basics concepts of structured knowledge representation
Learning how express uncertain knowledge using probability theory
Application of Bayes Theorem to computing conditional probabilities
Understanding the shortcoming of probability theory based knowledge representation and reasoning
Using Fuzzy Logic for representation of Imprecise and/or Uncertain knowledge
Explaining the basic concepts of a planning system
Discussing the distinction between problem solving as a search problem and planning system
Description of the meaning of planning in robotics
Understanding the role of knowledge Representation in a Natural Language Understanding Systems
Explaining techniques involved in implementing syntax checkers based on different type of grammars
