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