Introduction to Artificial Intelligence

• Introduction to Artificial Intelligence, examples of applications
• State space and basic search algorithms: depth-first, breadth-first and iterative deepening, complexity of these algorithms
• Heuristic search, algorithms A* and IDA*, admissibility theorem for A*, properties of heuristic function and analysis of time and space complexity
• Problem decomposition with AND/OR graphs, search in AND/OR graphs, heuristic search algorithm AO*
• Machine learning: problem of learning from data, data mining, description languages and hypothesis spaces, induction of decision trees, regression trees, model trees, and rules. Software tools for machine learning and applications.
• Knowledge representation and expert systems: knowledge representation with rules, frames, semantic networks, ontologies; inference algorithms and generationg explanation; handling uncertain knowledge, Bayesian networks
• Means-ends planning, total-order and partial-order planning, goal regression, applications in robotics and logistics

• Teach basic concepts, ideas, methods and techniques of artificial intelligence (AI)

• Ability to solve problems with methods of artificial intelligence

• Ability to understand the literature in the area of AI

• Contribute to the understanding of the relevance of technical achievements of AI with respect to their implications in philosophy and psychology

Lectures, laboratory exercises, homework, individual and team projects