Abstraktors

Abstraktors are a class of computational components designed to produce abstract representations of complex inputs. They aim to strip away incidental details while preserving core structure, relations, and higher-level semantics. The concept is used in discussions of cognitive modeling, AI planning, and data analysis to enable more scalable reasoning and transfer learning.

Design and operation: Abstraktors can be rule-based, statistical, or neural; they transform input X into an abstract representation Z = A(X) where A may be deterministic or stochastic. They may be trained with objectives that penalize loss of relational information while encouraging compact representations, such as through contrastive or variational objectives. They may be sequential or hierarchical, producing multi-level abstractions.

Types and examples: neural abstraktors include variational autoencoders or transformer-based abstraction modules; rule-based abstraktors implement domain knowledge to map raw signals to symbolic schemas; hybrid abstraktors combine both. In practice, they are used to improve planning, reasoning, anomaly detection, and explainability by providing human-interpretable summaries.

Relation to other concepts: Abstraktors are related to feature extractors, latent variable models, and symbolic AI, but emphasize abstraction over reconstruction, and aim to preserve structure rather than exact inputs. Evaluation is an area of active research, with metrics focusing on information preservation, interpretability, and downstream task performance.

History and usage: The term Abstraktor (plural Abstraktors) appeared in speculative AI literature in the early 2020s and has since been used in theoretical discussions and experimental systems exploring abstraction-driven intelligence.