alsoAnalogical

alsoAnalogical is a conceptual framework and software platform that advances analogical reasoning in artificial intelligence and cognitive science. It emphasizes cross-domain mappings of relational structures rather than relying on surface similarity, enabling problem solving, explanation, and learning through extended chains of analogy. The name signals both auxiliary reasoning and core relational mapping.

Originating in theoretical discussions of analogy, alsoAnalogical was proposed to formalize how multiple, layered analogies can be composed. It has been described in academic work as a modular approach that can be implemented on graph- or vector-based representations.

Core components include a mapping engine that computes correspondences between relational structures, an analogy catalog that stores prior mappings, and a retrieval component for cross-domain relevance. Representations may be graph-based or embedding-based, with explainability features that show alignment steps and justification for each inferred analogy.

Applications span natural language understanding, problem solving, education tools, and design ideation.

Reception is cautious: benefits include interpretability and transfer, while challenges involve scalability and data requirements. Ongoing work focuses on integrating learning signals and expanding benchmarks to validate cross-domain performance.