Knowledge Representation and Reasoning

The course aims to provide students with an in-depth understanding of the theoretical foundations and algorithms for knowledge representation and reasoning, focusing on the use of logical languages for encoding knowledge and symbolic inference techniques. Key symbolic AI concepts will be covered, such as formal explainability, the integration of deductive reasoning with AI approaches based on Machine Learning, and modern neuro-symbolic reasoning techniques. Practical applications of these techniques will also be explored.

The student will be able to use the main knowledge representation languages and encode various reasoning tasks using these languages. The student will be capable of modeling real-world scenarios with the learned languages and identifying the right trade-offs between expressiveness and computational complexity of the different languages. The student will also acquire an in-depth understanding of deductive reasoning algorithms and be able to use real systems for knowledge representation and reasoning, as well as integrate logic-based deduction techniques with inductive AI (i.e., ML) systems through neuro-symbolic reasoning approaches.