logiconmemory

Logiconmemory is a concept in computer science and cognitive science that describes a design which integrates logical reasoning with persistent memory storage. It envisions systems that maintain a structured, queryable memory of facts, rules, and inference traces while a reasoning component derives new conclusions from the stored knowledge. The term combines elements of logic-based computation with memory-centric architectures, aiming to support explainable, stateful artificial intelligence.

In typical proposals, logiconmemory consists of a memory subsystem that holds knowledge representations—often in the form of graphs, triples, or rule bases—and a reasoning engine that can query, update, and enforce consistency within that memory. Reasoning approaches may include rule-based engines, logic programming, or differentiable surrogates to balance interpretability with scalability. A memory controller handles indexing, provenance, versioning, and rollback, enabling traceable inference histories. Some hardware-oriented visions explore logic-in-memory or in-memory computing to reduce latency between storage and computation.

Operations in a logiconmemory framework emphasize persistent state and traceability. Inference results are stored alongside supporting evidence, enabling backtracking and auditability. The system can support non-monotonic reasoning, conflict resolution, and incremental updates as new information arrives. It is designed to facilitate modular knowledge maintenance, gradual learning, and interoperability with other data systems such as knowledge graphs or databases.

Applications include knowledge management, question answering, automated planning, reasoning over scientific data, and robotics. Potential benefits are improved explainability, reproducibility of inferences, and robust handling of symbolic information. Limitations involve scalability with large knowledge bases, maintaining data integrity during updates, and balancing symbolic reasoning with probabilistic methods.

See also: knowledge graphs, memory-augmented neural networks, symbolic AI, logic programming, external memory.