Introduction to Artificial Intelligence

The subject starts with a description of problem solving methods by means of heuristic search. Therafter, various knowledge representation languages and inference methods for automatic problem solving. Representation in form of predicate logic, frames and semantic nets are treated, and connected to the main forms of reasoning - especially rule based reasoning. Furthermore, architectures that integrates various resoning methods, agent based architectures and architectures for interactive problem solving. Numerous applicaton examples are given to demonstrate the methods.

Knowledge: The candidate will gain knowledge of:

• historical perspective of AI and its foundations
• basic principles of AI toward problem solving, inference, and knowledge representation
• representation and reasoning with propositional and predicate logic
• uninformed and heuristics search methods,
• adversarial search
• constraint satisfaction problems and methods
• representation of planning problems and solution methods
• multiagent environments and game theory principles and some problem solving methods
• ethics related problems in AI

Skills:

• decide which types of intelligence and agents are needed in a certain type of environment and design the agent accordingly
• design knowledge-based systems using the suitable type of representation, inference and problem solving method
• be able to identify possible ethical problems for a given a problem

General competence:

• Know AI's basis taken from logic and cognitive sciences.

Lectures, self study and exercises. A number of mandatory exercises must be approved in order to take the exam.