Lecturer: Prof. Dr. Christian Ressel
Lecturer: Prof. Dr. Kai Essig
Lecturer: Pedro Ribeiro, M.Sc.
Course material: HSRW - Moodle - Section
Study programs:
This course introduces students to the fundamentals of artificial intelligence. After a small introductory part to
the different aspects of AI, it focuses on the knowledge representation, inference / reasoning and autonomous
planning perspectives of AI systems. The topics are motivated by examples out of the domains of smart
environments, assistive systems and industry 4.0.
Having passed this module,students have acquired an understanding of AI‐technologies: its history, functionality
and potential, as well as its limitations. They are able to design systems that includes knowledge representation
as well as elements to infer knowledge and perform planning based on given information and conceptional
knowledge. However, students gained the fundamentals to develop these systems for fully observable,
deterministic environments as well as for partial observable and nondeterministic environments with uncertain
knowledge. Furthermore, the module enables students to develop or apply their own ideas in this field in
different contexts. Students also know about the societal context of AI. The learn to assess the ethical and social
impact of AI applications.
Have in mind there is an own machine learning course offered in the study program
Content:
- What is AI?: The history, vision, aspects and chances of AI in different domains
- Intelligent environments: Type of environments, typical elements, Context‐its value and inferring it from data
- Knowledge and reasoning: Propositional Logic, First‐Order Logic, Inference in First‐Order Logic,
- Knowledge Representation (Ontological Engineering)
- Planning with search: a) Using search algorithms to find action sequences: uniformed and informed strategies, heuristic functions, nondeterministic actions, partial observation b) Optimization problems, C) Constraint Satisfaction Problems, PDDL/ADL, Schedules and Resources, Hierarchical planning,
- Multiagent Planning
- Uncertain knowledge and reasoning: Probability Notation, Full joint distributions (and inferences),
- Independence, Bayes' Rule, Bayesian networks, Inference in Bayesian Networks, Relational and first‐order probability models, Utility Theory, Decision Networks
- Outlook, ethical and social impacts