Course Topics
Knowledge Representation introduces students to the foundational topics, methods, and theories in symbolic artificial intelligence, with a focus on how knowledge can be formally expressed and reasoned about. The course begins with a thorough exploration of classical first-order logic, covering its syntax, semantics, model theory, and proof theory, as a basis for knowledge formulation. It then moves on to Description Logics (DLs), which serve as a formal foundation for defining ontologies and knowledge bases. The module concludes with an analysis of key reasoning scenarios involving knowledge and ontologies, such as planning, non-monotonic reasoning, and common-sense inference, along with an introduction to selected non-classical reasoning approaches. Intelligent Agents Project focuses on the practical application of AI techniques to support human decision-making and to design autonomous systems. Students will actively engage in the development of software solutions that apply the AI methods introduced during the course to solve specific challenges. This hands-on experience is intended to deepen their understanding of various AI approaches and to highlight the complexities that arise in real-world applications. The goal of the module is to equip students with a versatile set of computational tools and methodologies, enabling them to address a range of practical problems and translate theoretical concepts into effective, working solutions.

Teaching format
Frontal lectures, exercises in lab, support for projects.

Educational objectives
Knowledge and Understanding – D1.13 Know the principles of artificial intelligence and potentials and limits of intelligent systems in various application domains. Applying knowledge and understanding – D2.2 Be able to develop small and medium size programs using different programming languages and paradigms. – D2.15 Be able to adopt programming techniques of artificial intelligence to solve problems of computer science. Ability to make judgments – D3.1 Be able to collect and interpret useful data and to judge information systems and their applicability. – D3.2 Be able to work autonomously according to the own level of knowledge and understanding. – D3.3 Be able to take the responsibility for development of projects or IT consulting. Communication skills – D4.1 Be able to use one of the three languages English, Italian and German, and be able to use technical terms and communication appropriately. – D4.4 Be able to structure and write technical documentation. – D4.5 Be able to work in teams for the realization of IT systems. Learning skills – D5.1 Have developed learning capabilities to pursue further studies with a high degree of autonomy. – D5.3 Be able to follow the fast technological evolution and to learn cutting edge IT technologies and innovative aspects of last generation information systems.

Assessment
Knowledge Representation: Lab exercises (30%) and final written exam (70%). Intelligent Agents Project: Lab assignments (60%) and oral presentation (40%)

Evaluation criteria
The final exam is assessed based on the correctness and clarity of answers, the ability to summarize and evaluate content, establish relationships between topics, demonstrate critical thinking, construct sound arguments, and solve problems. To pass the exam, students must achieve a minimum score of 18 out of 30 in each module. Each module contributes 50 percent to the overall grade. For Knowledge Representation, the evaluation is based on two components: lab exercises (30%) and on a written exam (70%). The exam assesses the clarity of responses, the mastery of terminology and definitions introduced during the course, and the ability to solve fundamental exercises or effectively summarize and illustrate theoretical concepts. For Intelligent Agents Project, the evaluation is divided into two components: lab assignments (60 percent) and an oral presentation (40 percent). The lab assignments are group-based and assessed on the originality and technical quality of the deliverables, the quality of documentation and presentation, and the group’s ability to collaborate effectively. Each assignment is evaluated individually, and the final lab score is the average of all submitted assignments. Missing an assignment deadline results in a score of zero for that task. To be admitted to the oral presentation, students must achieve at least 50 percent of the maximum possible score on the lab assignments. The oral presentation is an individual assessment that evaluates the student's ability to independently deepen their understanding of a selected course topic, summarize and connect relevant information, and clearly communicate their findings. The presentation topic must be agreed upon with the lecturer and must align with the course syllabus.

Required readings

–  David L. Poole and Alan K. Mackworth. Artificial Intelligence. Cambridge University Press, Cambridge, 3rd revised ed. edition edition, July 2023. ISBN 978-1-009-25819-7.

Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach. Pearson, Hoboken, 4th edition edition, April 2020. ISBN 978-0-13-461099-3.

Supplementary readings

Franz Baader, Ian Horrocks, Carsten Lutz, and Uli Sattler. An Introduction to Description Logic. Cambridge University Press, Cambridge, May 2017. ISBN 978-0-521-87361-1.

George Luger. Artificial Intelligence: Structures and Strategies for Complex Problem Solving. Pearson, Boston, 6th edition edition, February 2008. ISBN 978-0-321-54589-3.

Further information
Software Used: – Protégé (https://protege.stanford.edu) – ILTIS (https://iltis.rub.de) – DL Reasoners – Theorem provers – Python (https://www.python.org) – Domain-specific languages – Linux-based virtual machines