Current studies on non-systematic satisfiability in Discrete Hopfield Neural Network are able
to avoid production of repetitive final neuron states which improves the quality of global …

Abstract Argumentation frameworks (AFs) provide a central approach to perform reasoning
in many formalisms within argumentation in Artificial Intelligence (AI). Semantics for AFs …

Recently, a variety of non-systematic satisfiability studies on Discrete Hopfield Neural
Networks have been introduced to overcome a lack of interpretation. Although a flexible …

Forgetting–or variable elimination–is an operation that allows the removal, from a
knowledge base, of middle variables no longer deemed relevant. In recent years, many …

Abstraction is an important technique utilized by humans in model building and problem
solving, in order to figure out key elements and relevant details of a world of interest. This …

Simplification of logic programs under the answer-set semantics has been studied from the
very beginning of the field. One natural simplification is the removal of atoms that are …

Answer Set Programming (ASP) is a prominent rule-based language for knowledge
representation and reasoning with roots in logic programming and non-monotonic …

Abstraction is a commonly used process to represent some low-level system by a more
coarse specification with the goal to omit unnecessary details while preserving important …

Humans are capable of abstracting away irrelevant details when studying problems. This is
especially noticeable for problems over grid-cells, as humans are able to disregard certain …

Abstract Argumentation in Artificial Intelligence (AI) builds on formal approaches to
reasoning argumentatively. Common to many such approaches is to use argumentation …