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.