|Lecturer||Prof. Dr.-Ing. Matthias Althoff|
|Teaching Assistants||Aaron Pereira, Albert Rizaldi, Stefanie Manzinger, Silvia Magdici|
|Audience|| Obligatory in: Informatik Games Engineering; Robotics, Cognition, Intelligence; Automotive Software Engineering
Elective in: Informatik; Wirtschaftsinformatik; Physik; Technologie- u. Managementorientierte BWL
|Time & Place|| Wed 14:15 - 15:45 Friedrich L. Bauer Hörsaal (MI HS 1)
Fri 12:45 - 13:30 Gustav-Niemann-Hörsaal (MW 0001)
|Exercise||Fri 13:30 - 14:15 Gustav-Niemann-Hörsaal (MW 0001)|
- December 21, 2016: Lecture on the first hour and exercise next.
- December 16, 2016: We will have only exercise.
- December 14,2016: Exercise instead of lecture.
- December 02,2016: Lecture instead of exercise
- November 25,2016: Lecture on the first hour and exercise next
- November 23,2016: Exercise on Propositional Logic and First-Order Logic
- November 11, 2016: Instead of the exercise, we will have a lecture.
- November 02, 2016: Exercise instead of lecture.
- October 21, 2016: Instead of the exercise, we will have a lecture.
- October 19, 2016: The lecture starts
- The exam is on 21.02.2017, 08:00-09:30
- The repetition exam is on 24.04.2017, 17:00 - 18.30
- Task environments and the structure of intelligent agents.
- Solving problems by searching: breadth-first search, uniform-cost search, depth-first search, depth-limited search, iterative deepening search, greedy best-first search, A* search.
- Constraint satisfaction problems: defining constraint satisfaction problems, backtracking search for constraint satisfaction problems, heuristics for backtracking search, interleaving search and inference, the structure of constraint satisfaction problems.
- Logical agents: propositional logic, propositional theorem proving, syntax and semantics of first-order logic, using first-order logic, knowledge engineering in first-order logic, reducing first-order inference to propositional inference, unification and lifting, forward chaining, backward chaining, resolution.
- Bayesian networks: acting under uncertainty, basics of probability theory, Bayesian networks, inference in Bayesian networks, approximate inference in Bayesian networks.
- Hidden Markov models: time and uncertainty, inference in hidden Markov models (filtering, prediction, smoothing, most likely explanation), approximate inference in hidden Markov models.
- Rational decisions: introduction to utility theory, utility functions, decision networks, the value of information, Markov decision processes, value iteration, policy iteration, partially observable Markov decision processes.
- Learning: types of learning, supervised learning, learning decision trees.
- Introduction to robotics: robot hardware, robotic perception, path planning, planning uncertain movements, control of movements, robotic software architectures, application domains.
- The material is provided through the moodle website.
- Last year's moodle website (for long-term preview) is here.
- Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (3rd Edition). Prentice Hall, 2009. ISBN 0-13-604259-7.
- German edition: Russel/Norvig: Künstliche Intelligenz: Ein moderner Ansatz, 3. Auflage, Pearson, 2012