**1 Introduction.** The introductory lecture gives an overview of the course and introduces the subject of Artificial Intelligence (AI), in particular, knowledge-based systems. It presents popular AI systems, AI subdomains, relevant research directions and people.

**2 Propositional Logic.** This lecture explains logics and deduction, and introduces propositional logic. A detailed overview of propositional logic syntax, semantics and inference is provided.

**3 Predicate Logic.** This lecture explains restrictions of the propositional logic, motivates and introduces predicate logic. A detailed overview of predicate logic syntax, semantics and inference is provided.

**4 Reasoning.** This lecture provides foundations of reasoning, in particular, it covers Theorem Proving and Resolution, Description Logics, and Logic Programming.

**5 Search Methods.** The lecture presents search methods. Specifically, the mechanisms of uninformed search (depth-first search, breadth-first search), and informed search (best-first search, hill climbing, A* search) are described.

**6 CommonKADS.** This lecture focuses on CommonKADS methodology applicable to ontology engineering. It overviews knowledge model components, template knowledge models, knowledge model construction, knowledge elicitation techniques.

**7 Problem-Solving Methods.** The lecture elaborates the utilization of knowledge in reasoning and introduces general problem solvers. Approaches of development of knowledge-based systems towards problem solving methods and problem solving methods are the key topics.

**8 Planning.** This lecture considers planning as state-space search, for which there are several options: forward, backward, heuristic. It also introduces partial-order planning as a more flexible approach. In all cases there are three implicit concerns considered: representation, algorithm, complexity and decidability.

**9 Software Agents**. This lecture introduces agents, including definitions, properties, environments, agents as intentional systems, abstract architecture, multi-agent systems. A large example is given from Robocop.

**10 Rule Learning.** This lecture primarily addresses the domain of decision tree learning. The material addresses rule learning tasks, rule learning approach (specialization, i.e. the ID3 Algorithm; generalization, i.e. the RELAX Algorithm), combining specialization and generalization (the JoJo algorithm), refinement of rule sets with JoJo, as well as an extension – the C4.5 Algorithm.

**11 Inductive Logic Programming.** This lecture introduces Inductive Logic Programming (ILP) as a combination of inductive learning and logic programming. The contents of the lecture comprise model theory of ILP, a generic ILP algorithm, proof theory of ILP, ILP systems and applications.

**12 Formal Concept Analysis.** This lecture introduces formal concept analysis: introduction and definitions, attribute exploration, many-valued context. A larger example showcases the transformation from data to concept representation.

**13 Neural Networks.** The lecture presents neural networks: (artificial) neural networks, neural network structures, learning and generalization, expressiveness of multi-layer perceptrons. The given application illustrations comprise prediction of hazards, such as breast cancer reoccurrence.

**14 Semantic Web and Services.** This lecture presents a combination of the Semantic Web and services and their co-evolution. Core technology pillars are considered: URI, RDF, RDFS, OWL, SPARQL, and Semantic Web Services, WSMO, WSML, SEE, WSMX, as well as their applications.