Titlebook: Reasoning Web. Explainable Artificial Intelligence; 15th International S Markus Kr?tzsch,Daria Stepanova Textbook 2019 Springer Nature Swit

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Explainable AI Planning (XAIP): Overview and the Case of Contrastive Explanation (Extended Abstract recent works on one particular kind of XAIP, contrastive explanation. This extended abstract gives a brief summary of the lecture, with some literature pointers. We emphasize that completeness is neither claimed nor intended; the abstract may serve as a brief primer with literature entry points.

Counteract

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Constraint Learning: An Appetizer, learning methods, covering classic strategies and more recent advances. We will also discuss links to other areas of AI and machine learning, including concept learning, learning from queries, structured-output prediction, (statistical) relational learning, preference elicitation, and inverse optimization.

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,Classical Algorithms for Reasoning and?Explanation in Description Logics,ques based on keyword search or machine learning, the notion of a consequence is well-defined using a formal logic-based semantics. This course provides an in-depth description and analysis of the main reasoning and explanation methods for ontologies: tableau procedures and axiom pinpointing algorithms.

CLOUT

,Provenance in Databases: Principles and?Applications, explanation of query results. Though the theory of provenance semirings has mostly been developed in the setting of relational databases, it can also apply to other data representations, such as XML, graph, and triple-store databases.

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,Provenance in Databases: Principles and?Applications,ormal frameworks for data provenance have been proposed, in particular based on provenance semirings. The provenance of a query can be computed in these frameworks for a variety of query languages. Provenance has applications in various settings, such as probabilistic databases, view maintenance, or