Knowledge Representation and Reasoning

Goal

• Understand fundamental concepts for knowledge representation such as logic program, negation and negation as failure, open and closed world hypothesis, knowledge graphs, ontologies.
• Recognize and understand Knowledge Representation techniques and their utilization in problem solving / application contexts.
• It points out the specificity of individual reasoning problems, the selection and adaptation to them of appropriate search techniques, constraint solving, answer set solving and preferences.
• Plan the evaluation of the methods in comparison with each other, recognizing the possibilities and limitations of each method.
• Understand the structure of the semantic web and how its basic tools work.
• Communicate ideas related to the application of knowledge representation techniques and reasoning in a clear, concise and formal manner. The student must be able to design, build and evaluate knowledge representation and reasoning systems for solving real-world problems, and to explain their operation.

• Ability to organize and plan work, and manage time effectively
• Ability to communicate orally and in writing
• Ability to solve problems
• Ability to develop critical thinking and capacity for critical approaches
• Ability to apply theoretical knowledge in practice
• Ability to adapt methods and techniques to new situations and conditions
• Exercise criticism and self-criticism
• Promotion of free, creative and inductive thinking

Contents

• Introduction to Logic Programming: Facts, rules and queries, recursion, lists and functional terms, reversible predicates
• LP for AI: Non-deterministic programming, generate-and-test, searching in Prolog
• Approaches to negation: Negation-as-failure, stratification, well-founded negation in non-stratified knowledge bases
• Representing incomplete knowledge: Answer Set Programming, integrity constraints, stable models, satisfiability, enumeration, answer set solving
• Representing preferences: quantitative vs. qualitative preferences, logical formalisms for qualitative preference representation, preferences between sets
• KRR for the Semantic Web: data integration and the semantics of atomic symbols, representing Web semantics, Resource Description Framework (RDF), ontologies and knowledge graphs (KG), OWL 2, Description Logics and equivalence with RDFS/OWL 2, reasoning over RDFS/OWL 2
• Representing and reasoning over spatial and numerical knowledge: Many-valued logics, Satisfiability Modulo Theories (SMT), geospatial data and inference, 3D data and inference

Bibliography

• Lloyd, John W. Foundations of logic programming. Springer Science & Business Media, 2012.
• O’Keefe, Richard. The craft of Prolog. Logic programming.  MIT Press, 1990.
• Apt, Krzysztof R., and Mark Wallace. Constraint logic programming using ECLiPSe. Cambridge University Press, 2006.
• Baral, Chitta. Knowledge representation, reasoning and declarative problem solving with Answer sets, 2001
• Guus Schreiber and Yves Raimond (eds), RDF 1.1 Primer. W3C Working Group Note, 24 June 2014, https://www.w3.org/TR/rdf11-primer
• Pascal Hitzler, Markus Krötzsch, Bijan Parsia, Peter F. Patel-Schneider, and Sebastian Rudolph (eds), OWL 2 Web Ontology Language Primer (Second Edition). W3C Recommendation, 11 December 2012, http://www.w3.org/TR/owl2-primer
• Tom Mitchell, “Learning sets of rules”. Chapter 10, Machine Learning, McGraw Hill, 1997.
• Stasinos Konstantopoulos, Rui Camacho, Nuno Fonseca and Vitor Santos Costa, “Induction as a search procedure”. Chapter 7, Artificial Intelligence for Advanced Problem Solving Techniques. Information Science Reference, IGΙ Global, 2008.